Setups

Loading packages and custom functions

# loading packages
#devtools::install_github("itchyshin/orchard_plot", subdir = "orchaRd", force = TRUE, build_vignettes = TRUE)
pacman::p_load(SciViews,
               tidyverse,
               dplyr,
               metafor, # package for meta-analysis
               rotl, # package for phylogeny
               ape, # package for phylogeny
               cowplot, # combining multiple plots
               here, # making folder path usable for all
               clubSandwich, # package to assist metafor
               orchaRd, # plotting orchaRd plots
               MuMIn, # multi-model inference
               kableExtra, # making nice tables
               patchwork, # putting ggplots together
               png, # reading png files
               grid, # graphic layout manipulation
               pander, # nice tables
               ggplot2 # making figures
)

Custom functions

We have 4 custom functions named : Zr_transformation(), Calc_SV(), Zr_to_ICC(), and cont_gen(), all of which are used later (see below for their functionality) and the code is included here.

# Custom functions

# Getting Zr and its sampling variance from repeatability values and sample
# size information Function for Fisher's Z transformation (Zr) for
# correlation-based repeatabilities (r) and ICC (Holtmann et al. 2017, Table 1)
Zr_transformation <- function(r, K, Est) {

    if (Est == "ICC") {
        Zr <- 0.5 * ln(((1 + (K - 1) * r)/(1 - r)))
    }
    if (Est == "r") {
        Zr <- 0.5 * ln(((1 + r)/(1 - r)))
    }
    Zr
}

# Function for sampling variance for correlation-based repeatabilities (r) and
# ICC (Holtmann et al. 2017, Table 1)
Calc_SV <- function(K, N, Est) {

    if (Est == "ICC") {
        VZr <- K/(2 * ((N - 2) * (K - 1)))
    }
    if (Est == "r") {
        VZr <- 1/(N - 3)
    }
    VZr
}

# Function for back-transforming Zr to ICC
Zr_to_ICC <- function(x, k) {
    (exp(2 * x) - 1)/(exp(2 * x) + k - 1)
}

# Contrast name generator for tibble tables (from Hayward et al. 2021)
cont_gen <- function(name) {
    combination <- combn(name, 2)
    name_dat <- t(combination)
    names <- paste(name_dat[, 1], name_dat[, 2], sep = "-")
    return(names)
}

Supplementary Methods

PRISMA flow chart

Figure S1

Figure S1. PRISMA flow chart summarising search methods and screening for studies included in analyses, and reasons for excluding studies.

Decision tree

Figure S2

Figure S2. Decision tree used to evaluate studies for inclusion and exclusion at the stage of title and abstract screening.

The Repeatability Dataset

Table of the dataset

Below is the dataset used for our meta-analysis, followed by explanations of the variables extracted from the papers included (not all variables were used for our analyses).

Table S1

The meta-analytic dataset of this study.

# getting the data and formating some variables (turning chraracter vectors to
# factors)
df <- read_csv(here("data", "Meta-analysis_data-2022.csv"), na = "NA", show_col_types = F) %>%
    mutate_if(is.character, as.factor)

# making a scrollable table of dataset
kable(df, "html") %>%
    kable_styling("striped", position = "left") %>%
    scroll_box(width = "100%", height = "500px")
es_ID paper_ID cohort_ID species_ID species_common species_latin taxa sex n k est R fixed_yn fixed_var unstandardized_variance method annual_event location country long lat tag_period notes data_location data_presentation title pub_year authors journal DOI Papers for Data Extraction::fulltext_ID
es001 p001 c001 s039 American redstart Setophaga ruticilla Landbird B 74 2.64 ICC 0.3820 N NA no Conventional Nonbreed_depart southwestern Jamaica at the Font Hill Nature reserve North America -77.950000 18.03000 Non-breeding p. 3440, text, results text Rainfall-induced changes in food availability modify the spring departure programme of a migratory bird 2011 Studds, C. E. and Marra, P. P. Proceedings of the Royal Society B: Biological Sciences 10.1098/rspb.2011.0332 ft161
es002 p002 c002 s029 American white pelican Pelecanus erythrorhynchos Waterbird B 12 2.00 ICC 0.0200 N NA no Satellite Depart_breed Multiple areas in US North America -89.883000 32.48000 Non-breeding Table 1, p. 4 text Advances and environmental conditions of spring migration phenology of American white pelicans 2017 King, D.T. and Wang, G. and Yang, Z. and Fischer, J. W. Scientific Reports 10.1038/srep40339 ft163
es003 p002 c002 s029 American white pelican Pelecanus erythrorhynchos Waterbird B 12 2.00 ICC 0.3180 N NA no Satellite Arrival_breed Multiple areas in US North America -89.883000 32.48000 Non-breeding Table 1, p. 4 text Advances and environmental conditions of spring migration phenology of American white pelicans 2017 King, D.T. and Wang, G. and Yang, Z. and Fischer, J. W. Scientific Reports 10.1038/srep40339 ft163
es004 p002 c002 s029 American white pelican Pelecanus erythrorhynchos Waterbird B 12 2.00 ICC 0.3670 N NA no Satellite Nonbreed_arrival Multiple areas in US North America -89.883000 32.48000 Non-breeding Table 1, p. 4 text Advances and environmental conditions of spring migration phenology of American white pelicans 2017 King, D.T. and Wang, G. and Yang, Z. and Fischer, J. W. Scientific Reports 10.1038/srep40339 ft163
es005 p002 c002 s029 American white pelican Pelecanus erythrorhynchos Waterbird B 12 2.00 ICC 0.7630 N NA no Satellite Nonbreed_depart Multiple areas in US North America -89.883000 32.48000 Non-breeding Table 1, p. 4 text Advances and environmental conditions of spring migration phenology of American white pelicans 2017 King, D.T. and Wang, G. and Yang, Z. and Fischer, J. W. Scientific Reports 10.1038/srep40339 ft163
es006 p003 c003 s016 Barn swallow Hirundo rustica Landbird M 15 2.00 ICC 0.5000 N NA no Conventional Arrival_breed Badajoz, Southern Spain Europe -6.490000 38.53000 Breeding p. 58, text, results text Antioxidants and condition-dependence of arrival date in a migratory passerine 2004 Ninni, P. and de Lope, F. and Saino, N. and Haussy, C. and Moller, A. P. Oikos 10.1111/j.0030-1299.2004.12516.x ft012
es007 p004 c004 s016 Barn swallow Hirundo rustica Landbird M 23 5.20 ICC 0.5100 N NA no Conventional Arrival_breed Kraghede, Denmark Europe 10.000000 57.20000 Breeding p. 204, text, results text Heritability of arrival date in a migratory bird 2001 Moller, A. P. Proceedings of the Royal Society B-Biological Sciences 10.1098/rspb.2000.1351 ft062
es008 p005 c005 s023 Bar-tailed godwit Limosa lapponica baueri Waterbird B 11 2.00 ICC 0.3800 N NA no Conventional Nonbreed_depart western Firth of Thames, NZ Not 175.316000 -37.18300 Non-breeding p. 518, text, results text Consistent annual schedules in a migratory shorebird 2006 Battley, P. F. Biology Letters 10.1098/rsbl.2006.0535 ft029
es009 p006 c006 s023 Bar-tailed godwit Limosa lapponica baueri Waterbird B 6 2.00 ICC 0.4700 N NA yes GLS Depart_breed Manawatu River estuary, New Zealand Not 175.360000 -40.78300 Non-breeding Table 1, p. 6 text Absolute Consistency: Individual versus Population Variation in Annual-Cycle Schedules of a Long-Distance Migrant Bird 2013 Conklin, J. R. and Battley, P. F. and Potter, M. A. PLoS ONE 10.1371/journal.pone.0054535 ft001
es010 p006 c007 s023 Bar-tailed godwit Limosa lapponica baueri Waterbird B 44 2.00 ICC 0.6600 N NA yes Conventional Nonbreed_arrival Manawatu River estuary, New Zealand Not 175.360000 -40.78300 Non-breeding Table 1, p. 6 text Absolute Consistency: Individual versus Population Variation in Annual-Cycle Schedules of a Long-Distance Migrant Bird 2013 Conklin, J. R. and Battley, P. F. and Potter, M. A. PLoS ONE 10.1371/journal.pone.0054535 ft001
es011 p006 c006 s023 Bar-tailed godwit Limosa lapponica baueri Waterbird B 8 2.00 ICC 0.7700 N NA yes GLS Nonbreed_arrival Manawatu River estuary, New Zealand Not 175.360000 -40.78300 Non-breeding Table 1, p. 6 text Absolute Consistency: Individual versus Population Variation in Annual-Cycle Schedules of a Long-Distance Migrant Bird 2013 Conklin, J. R. and Battley, P. F. and Potter, M. A. PLoS ONE 10.1371/journal.pone.0054535 ft001
es012 p006 c007 s023 Bar-tailed godwit Limosa lapponica baueri Waterbird B 49 2.00 ICC 0.8200 N NA yes Conventional Nonbreed_depart Manawatu River estuary, New Zealand Not 175.360000 -40.78300 Non-breeding Table 1, p. 6 text Absolute Consistency: Individual versus Population Variation in Annual-Cycle Schedules of a Long-Distance Migrant Bird 2013 Conklin, J. R. and Battley, P. F. and Potter, M. A. PLoS ONE 10.1371/journal.pone.0054535 ft001
es013 p005 c005 s023 Bar-tailed godwit Limosa lapponica baueri Waterbird B 38 2.20 ICC 0.8300 N NA no Conventional Nonbreed_depart western Firth of Thames, NZ Not 175.316000 -37.18300 Non-breeding p. 518, text, results text Consistent annual schedules in a migratory shorebird 2006 Battley, P. F. Biology Letters 10.1098/rsbl.2006.0535 ft029
es014 p007 c008 s023 Bar-tailed godwit Limosa lapponica baueri Waterbird B 45 3.00 ICC 0.8360 N NA yes Conventional Depart_breed Manawatu River estuary, New Zealand Not 175.360000 -40.78300 Non-breeding p. 856, text, results text Impacts of wind on individual migration schedules of New Zealand bar-tailed godwits 2011 Conklin, J. R. and Battley, P. F. Behavioral Ecology 10.1093/beheco/arr054 ft065
es015 p006 c006 s023 Bar-tailed godwit Limosa lapponica baueri Waterbird B 8 2.00 ICC 0.8600 N NA yes GLS Nonbreed_depart Manawatu River estuary, New Zealand Not 175.360000 -40.78300 Non-breeding Table 1, p. 6 text Absolute Consistency: Individual versus Population Variation in Annual-Cycle Schedules of a Long-Distance Migrant Bird 2013 Conklin, J. R. and Battley, P. F. and Potter, M. A. PLoS ONE 10.1371/journal.pone.0054535 ft001
es016 p006 c006 s023 Bar-tailed godwit Limosa lapponica baueri Waterbird B 8 2.00 ICC 0.9100 N NA yes GLS Arrival_breed Manawatu River estuary, New Zealand Not 175.360000 -40.78300 Non-breeding Table 1, p. 6 text Absolute Consistency: Individual versus Population Variation in Annual-Cycle Schedules of a Long-Distance Migrant Bird 2013 Conklin, J. R. and Battley, P. F. and Potter, M. A. PLoS ONE 10.1371/journal.pone.0054535 ft001
es017 p008 c009 s012 Bewick swan Cygnus columbianus bewickii Waterbird B 67 12.40 ICC 0.0100 N NA yes Conventional Nonbreed_arrival Slimbridge Europe -2.400000 51.74000 Non-breeding calculated values from Table 1, p. 388 text Consistency in the timing of migration for individual Bewick’s swans 1989 Rees, E. C. Animal Behaviour 10.1016/S0003-3472(89)80031-4 ft028
es018 p008 c009 s012 Bewick swan Cygnus columbianus bewickii Waterbird B 67 12.40 ICC 0.0300 N NA yes Conventional Nonbreed_depart Slimbridge Europe -2.400000 51.74000 Non-breeding calculated values from Table 1, p. 388 text Consistency in the timing of migration for individual Bewick’s swans 1989 Rees, E. C. Animal Behaviour 10.1016/S0003-3472(89)80031-4 ft028
es019 p009 c010 s037 Black-legged kittiwake Rissa tridactyla Seabird B 48 2.00 ICC 0.3900 Y AnyChick+Sex+Treatment+AnyChick*Treatment + (1|Band) + (1|Year) no GLS Arrival_breed Middleton Island North America -146.320000 59.44000 Breeding p. 3, text, results text Increased summer food supply decreases non-breeding movement in black-legged kittiwakes 2020 Whelan, S. and Hatch, S. A. and Irons D. B. and McKnight A. and Elliott K. H. Biology Letters 10.1098/rsbl.2019.0725 ft165
es020 p009 c010 s037 Black-legged kittiwake Rissa tridactyla Seabird B 48 2.00 ICC 0.7390 Y AnyChick+Sex+Treatment+AnyChick*Treatment + (1|Band) + (1|Year) no GLS Depart_breed Middleton Island North America -146.320000 59.44000 Breeding p. 3, text, results text Increased summer food supply decreases non-breeding movement in black-legged kittiwakes 2020 Whelan, S. and Hatch, S. A. and Irons D. B. and McKnight A. and Elliott K. H. Biology Letters 10.1098/rsbl.2019.0725 ft165
es021 p010 c011 s024 Black-tailed godwit Limosa limosa limosa Waterbird B 36 2.30 ICC 0.1390 N NA yes GLS Depart_breed southwest Fryslan, The Netherlands Europe 5.540000 53.50000 Breeding Figure 2. E, p. 5 figure Variation From an Unknown Source: Large Inter-individual Differences in Migrating Black-Tailed Godwits 2019 Verhoeven, M. A. and Loonstra, A. H. J. and Senner, N. R. and McBride, A. D. and Both, C. and Piersma, T. Frontiers in Ecology and Evolution 10.3389/fevo.2019.00031 ft142
es022 p011 c012 s024 Black-tailed godwit Limosa limosa islandica Waterbird B 46 2.46 ICC 0.1800 N NA no Conventional Arrival_breed Iceland Europe -21.747000 64.45400 Breeding p. 1122, text, results text Population-scale drivers of individual arrival times in migratory birds 2006 Gunnarsson, T. G. and Gill, J. A. and Atkinson, P. W. and Gelinaud, G. and Potts, P. M. and Croger, R. E. and Gudmundsson, G. A. and Appleton, G. F. and Sutherland, W. J. Journal of Animal Ecology 10.1111/j.1365-2656.2006.01131.x ft116
es023 p012 c013 s024 Black-tailed godwit Limosa limosa limosa Waterbird M 70 3.30 ICC 0.1800 N NA no Conventional Arrival_breed Workumerwaard, the Netherlands Europe 5.400000 52.40000 both? Table 1, p. 1027 text Repeatable timing of northward departure, arrival and breeding in Black-tailed Godwits Limosa l. limosa, but no domino effects 2011 Louren<e7>o, P. M. and Kentie, R. and Schroeder, J. and Groen, N. M. and Hooijmeijer, J. C. E. W. and Piersma, T. Journal of Ornithology 10.1007/s10336-011-0692-3 ft121
es024 p013 c015 s024 Black-tailed godwit Limosa limosa limosa Waterbird B 180 3.10 ICC 0.2400 N NA no Conventional Arrival_breed southwest Friesland, the Netherlands Europe 5.083000 52.91600 both? p. 2816, text, results text Does wintering north or south of the Sahara correlate with timing and breeding performance in black-tailed godwits? 2017 Kentie, R. and Marquez-Ferrando, R. and Figuerola, J. and Gangoso, L. and Hooijmeijer, J. C. E. W. and Loonstra, A. H. J. and Robin, F. and Sarasa, M. and Senner, N. and Valkema, H. and Verhoeven, M. A. and Piersma, T. Ecology and Evolution 10.1002/ece3.2879 ft041
es025 p013 c016 s024 Black-tailed godwit Limosa limosa limosa Waterbird B 131 2.70 ICC 0.2400 N NA no Conventional Arrival_breed southwest Friesland, the Netherlands Europe 5.083000 52.91600 both? p. 2816, text, results text Does wintering north or south of the Sahara correlate with timing and breeding performance in black-tailed godwits? 2017 Kentie, R. and Marquez-Ferrando, R. and Figuerola, J. and Gangoso, L. and Hooijmeijer, J. C. E. W. and Loonstra, A. H. J. and Robin, F. and Sarasa, M. and Senner, N. and Valkema, H. and Verhoeven, M. A. and Piersma, T. Ecology and Evolution 10.1002/ece3.2879 ft041
es026 p012 c014 s024 Black-tailed godwit Limosa limosa limosa Waterbird F 81 2.90 ICC 0.2900 N NA no Conventional Arrival_breed Workumerwaard, the Netherlands Europe 5.400000 52.40000 both? Table 1, p. 1027 text Repeatable timing of northward departure, arrival and breeding in Black-tailed Godwits Limosa l. limosa, but no domino effects 2011 Louren<e7>o, P. M. and Kentie, R. and Schroeder, J. and Groen, N. M. and Hooijmeijer, J. C. E. W. and Piersma, T. Journal of Ornithology 10.1007/s10336-011-0692-3 ft121
es027 p010 c011 s024 Black-tailed godwit Limosa limosa limosa Waterbird B 24 2.10 ICC 0.3330 N NA yes GLS Arrival_breed southwest Fryslan, The Netherlands Europe 5.540000 53.50000 Breeding Figure 2. F, p. 5 figure Variation From an Unknown Source: Large Inter-individual Differences in Migrating Black-Tailed Godwits 2019 Verhoeven, M. A. and Loonstra, A. H. J. and Senner, N. R. and McBride, A. D. and Both, C. and Piersma, T. Frontiers in Ecology and Evolution 10.3389/fevo.2019.00031 ft142
es028 p014 c017 s024 Black-tailed godwit Limosa limosa islandica Waterbird B 54 5.40 ICC 0.5100 N NA no Conventional Arrival_breed Iceland Europe -17.190000 64.58000 Breeding p. 3, text, results text Why is timing of bird migration advancing when individuals are not? 2014 Gill, J. A. and Alves, J. A. and Sutherland, W. J. and Appleton, G. F. and Potts, P. M. and Gunnarsson, T. G. Proceedings of the Royal Society B: Biological Sciences 10.1098/rspb.2013.2161 ft150
es029 p010 c011 s024 Black-tailed godwit Limosa limosa limosa Waterbird B 29 2.30 ICC 0.6030 N NA yes GLS Nonbreed_arrival southwest Fryslan, The Netherlands Europe 5.540000 53.50000 Breeding Figure 2. E, p. 5 figure Variation From an Unknown Source: Large Inter-individual Differences in Migrating Black-Tailed Godwits 2019 Verhoeven, M. A. and Loonstra, A. H. J. and Senner, N. R. and McBride, A. D. and Both, C. and Piersma, T. Frontiers in Ecology and Evolution 10.3389/fevo.2019.00031 ft142
es030 p010 c011 s024 Black-tailed godwit Limosa limosa limosa Waterbird B 25 2.00 ICC 0.9370 N NA yes GLS Nonbreed_depart southwest Fryslan, The Netherlands Europe 5.540000 53.50000 Breeding Figure 2. F, p. 5 figure Variation From an Unknown Source: Large Inter-individual Differences in Migrating Black-Tailed Godwits 2019 Verhoeven, M. A. and Loonstra, A. H. J. and Senner, N. R. and McBride, A. D. and Both, C. and Piersma, T. Frontiers in Ecology and Evolution 10.3389/fevo.2019.00031 ft142
es031 p015 c018 s008 Brown skua Catharacta antarctica lonnbergi Seabird F 11 2.10 ICC 0.4770 Y year no GLS Depart_breed King George Island Not -59.583300 -62.31660 Breeding Table 1, p. 220 text Consistent variation in individual migration strategies of brown skuas 2017 Krietsch, J. and Hahn, S. and Kopp, M. and Phillips, R. A. and Peter, H. U. and Lisovski, S. Marine Ecology Progress Series 10.3354/meps11932 ft032
es032 p015 c019 s008 Brown skua Catharacta antarctica lonnbergi Seabird M 4 2.50 ICC 0.4900 Y year no GLS Depart_breed King George Island Not -59.583300 -62.31660 Breeding Table 1, p. 220 text Consistent variation in individual migration strategies of brown skuas 2017 Krietsch, J. and Hahn, S. and Kopp, M. and Phillips, R. A. and Peter, H. U. and Lisovski, S. Marine Ecology Progress Series 10.3354/meps11932 ft032
es033 p015 c018 s008 Brown skua Catharacta antarctica lonnbergi Seabird F 12 2.10 ICC 0.8710 Y year no GLS Arrival_breed King George Island Not -59.583300 -62.31660 Breeding Table 1, p. 220 text Consistent variation in individual migration strategies of brown skuas 2017 Krietsch, J. and Hahn, S. and Kopp, M. and Phillips, R. A. and Peter, H. U. and Lisovski, S. Marine Ecology Progress Series 10.3354/meps11932 ft032
es034 p015 c019 s008 Brown skua Catharacta antarctica lonnbergi Seabird M 4 2.50 ICC 0.9720 Y year no GLS Arrival_breed King George Island Not -59.583300 -62.31660 Breeding Table 1, p. 220 text Consistent variation in individual migration strategies of brown skuas 2017 Krietsch, J. and Hahn, S. and Kopp, M. and Phillips, R. A. and Peter, H. U. and Lisovski, S. Marine Ecology Progress Series 10.3354/meps11932 ft032
es035 p016 c020 s013 Collared flycatcher Ficedula albicollis Landbird M 106 2.20 ICC 0.2400 N NA yes Conventional Arrival_breed Czechia Europe 17.220000 49.83000 Breeding text, results, last paragraph text The genetic regulation of avian migration timing: combining candidate genes and quantitative genetic approaches in a long-distance migrant 2021 Krist, M. and Munclinger, P. and Briedis, M. and Adamík, P. Oecologia 10.1007/s00442-021-04930-x ft056
es036 p017 c021 s045 Common murre Uria aalge Seabird B 7 1.40 ICC 0.0100 N NA no GLS Depart_breed 7 seabird colonies ranging 74 to 47 N North America -82.970000 62.54000 Breeding Table 2, p. 7 text Individual winter movement strategies in two species of murre (Uria spp.) in the Northwest Atlantic 2014 McFarlane Tranquilla, L. A. and Montevecchi, W. A. and Fifield, D. A. and Hedd, A. and Gaston, A. J. and Robertson, G. J. and Phillips, R. A. PLoS ONE 10.1371/journal.pone.0090583 ft081
es037 p017 c022 s045 Common murre Uria aalge Seabird B 7 2.30 ICC 0.3500 N NA no GLS Arrival_breed 7 seabird colonies ranging 74 to 47 N North America -82.970000 62.54000 Breeding Table 2, p. 7 text Individual winter movement strategies in two species of murre (Uria spp.) in the Northwest Atlantic 2014 McFarlane Tranquilla, L. A. and Montevecchi, W. A. and Fifield, D. A. and Hedd, A. and Gaston, A. J. and Robertson, G. J. and Phillips, R. A. PLoS ONE 10.1371/journal.pone.0090583 ft081
es038 p017 c023 s045 Common murre Uria aalge Seabird B 6 2.70 ICC 0.4200 N NA no GLS Arrival_breed 7 seabird colonies ranging 74 to 47 N North America -82.970000 62.54000 Breeding Table 2, p. 7 text Individual winter movement strategies in two species of murre (Uria spp.) in the Northwest Atlantic 2014 McFarlane Tranquilla, L. A. and Montevecchi, W. A. and Fifield, D. A. and Hedd, A. and Gaston, A. J. and Robertson, G. J. and Phillips, R. A. PLoS ONE 10.1371/journal.pone.0090583 ft081
es039 p017 c022 s045 Common murre Uria aalge Seabird B 7 1.10 ICC 0.5600 N NA no GLS Depart_breed 7 seabird colonies ranging 74 to 47 N North America -82.970000 62.54000 Breeding Table 2, p. 7 text Individual winter movement strategies in two species of murre (Uria spp.) in the Northwest Atlantic 2014 McFarlane Tranquilla, L. A. and Montevecchi, W. A. and Fifield, D. A. and Hedd, A. and Gaston, A. J. and Robertson, G. J. and Phillips, R. A. PLoS ONE 10.1371/journal.pone.0090583 ft081
es040 p017 c021 s045 Common murre Uria aalge Seabird B 7 1.10 ICC 0.6400 N NA no GLS Arrival_breed 7 seabird colonies ranging 74 to 47 N North America -82.970000 62.54000 Breeding Table 2, p. 7 text Individual winter movement strategies in two species of murre (Uria spp.) in the Northwest Atlantic 2014 McFarlane Tranquilla, L. A. and Montevecchi, W. A. and Fifield, D. A. and Hedd, A. and Gaston, A. J. and Robertson, G. J. and Phillips, R. A. PLoS ONE 10.1371/journal.pone.0090583 ft081
es041 p017 c023 s045 Common murre Uria aalge Seabird B 6 3.00 ICC 0.8200 N NA no GLS Depart_breed 7 seabird colonies ranging 74 to 47 N North America -82.970000 62.54000 Breeding Table 2, p. 7 text Individual winter movement strategies in two species of murre (Uria spp.) in the Northwest Atlantic 2014 McFarlane Tranquilla, L. A. and Montevecchi, W. A. and Fifield, D. A. and Hedd, A. and Gaston, A. J. and Robertson, G. J. and Phillips, R. A. PLoS ONE 10.1371/journal.pone.0090583 ft081
es042 p018 c027 s003 Common swift Apus apus Landbird M 3 2.00 ICC -0.4500 N NA no GLS Depart_breed near Olpe, Germany Europe 7.816600 51.03300 Breeding Table 2, p. 900 text ‘Same procedure as last year?’ Repeatedly tracked swifts show individual consistency in migration pattern in successive years 2017 Wellbrock, A. H. J. and Bauch, C. and Rozman, J. and Witte, K. Journal of Avian Biology 10.1111/jav.01251 ft123
es043 p018 c027 s003 Common swift Apus apus Landbird M 3 2.00 ICC -0.0700 N NA no GLS Arrival_breed near Olpe, Germany Europe 7.816600 51.03300 Breeding Table 2, p. 900 text ‘Same procedure as last year?’ Repeatedly tracked swifts show individual consistency in migration pattern in successive years 2017 Wellbrock, A. H. J. and Bauch, C. and Rozman, J. and Witte, K. Journal of Avian Biology 10.1111/jav.01251 ft123
es044 p018 c027 s003 Common swift Apus apus Landbird M 3 2.00 ICC 0.6700 N NA no GLS Nonbreed_arrival near Olpe, Germany Europe 7.816600 51.03300 Breeding Table 2, p. 900 text ‘Same procedure as last year?’ Repeatedly tracked swifts show individual consistency in migration pattern in successive years 2017 Wellbrock, A. H. J. and Bauch, C. and Rozman, J. and Witte, K. Journal of Avian Biology 10.1111/jav.01251 ft123
es045 p019 c028 s041 Common tern Sterna hirundo Seabird B 1232 4.30 ICC 0.2000 Y Reproductive stage yes Conventional Arrival_breed Wilhelmshaven, Germany Europe 8.099000 53.50500 Breeding Age includes first time-breeders, inexperiend and experienced p. 686, text, results text Canalization of phenology in common terns: Genetic and phenotypic variations in spring arrival date 2013 Arnaud, C. M. and Becker, P. H. and Dobson F. S. and Charmantier A. Behavioral Ecology 10.1093/beheco/ars214 ft162
es046 p019 c029 s041 Common tern Sterna hirundo Seabird B 648 4.40 ICC 0.3500 Y Age, sex, breeding success in previous year yes Conventional Arrival_breed Wilhelmshaven, Germany Europe 8.099000 53.50500 Breeding Experienced breeders only p. 686, text, results text Canalization of phenology in common terns: Genetic and phenotypic variations in spring arrival date 2013 Arnaud, C. M. and Becker, P. H. and Dobson F. S. and Charmantier A. Behavioral Ecology 10.1093/beheco/ars214 ft162
es047 p020 c030 s035 Desertas petrel Pterodroma deserta Seabird B 4 2.00 ICC 0.4900 N NA yes GLS Arrival_breed Bugio Island, Madeira Europe -16.480000 32.43000 Breeding Table 2, p. 146 text Conservation implications of consistent foraging and trophic ecology in a rare petrel species 2016 Ramirez, I. and Paiva, V. H. and Fagundes, I. and Menezes, D. and Silva, I. and Ceia, F. R. and Phillips, R. A. and Ramos, J. A. and Garthe, S. Animal Conservation 10.1111/acv.12227 ft025
es048 p020 c030 s035 Desertas petrel Pterodroma deserta Seabird B 4 2.00 ICC 0.5600 N NA yes GLS Depart_breed Bugio Island, Madeira Europe -16.480000 32.43000 Breeding Table 2, p. 146 text Conservation implications of consistent foraging and trophic ecology in a rare petrel species 2016 Ramirez, I. and Paiva, V. H. and Fagundes, I. and Menezes, D. and Silva, I. and Ceia, F. R. and Phillips, R. A. and Ramos, J. A. and Garthe, S. Animal Conservation 10.1111/acv.12227 ft025
es049 p020 c031 s035 Desertas petrel Pterodroma deserta Seabird B 6 2.50 ICC 0.6200 N NA yes GLS Depart_breed Bugio Island, Madeira Europe -16.480000 32.43000 Breeding Table 2, p. 146 text Conservation implications of consistent foraging and trophic ecology in a rare petrel species 2016 Ramirez, I. and Paiva, V. H. and Fagundes, I. and Menezes, D. and Silva, I. and Ceia, F. R. and Phillips, R. A. and Ramos, J. A. and Garthe, S. Animal Conservation 10.1111/acv.12227 ft025
es050 p020 c031 s035 Desertas petrel Pterodroma deserta Seabird B 6 2.50 ICC 0.6300 N NA yes GLS Arrival_breed Bugio Island, Madeira Europe -16.480000 32.43000 Breeding Table 2, p. 146 text Conservation implications of consistent foraging and trophic ecology in a rare petrel species 2016 Ramirez, I. and Paiva, V. H. and Fagundes, I. and Menezes, D. and Silva, I. and Ceia, F. R. and Phillips, R. A. and Ramos, J. A. and Garthe, S. Animal Conservation 10.1111/acv.12227 ft025
es051 p020 c032 s035 Desertas petrel Pterodroma deserta Seabird B 5 2.20 ICC 0.6700 N NA yes GLS Depart_breed Bugio Island, Madeira Europe -16.480000 32.43000 Breeding Table 2, p. 146 text Conservation implications of consistent foraging and trophic ecology in a rare petrel species 2016 Ramirez, I. and Paiva, V. H. and Fagundes, I. and Menezes, D. and Silva, I. and Ceia, F. R. and Phillips, R. A. and Ramos, J. A. and Garthe, S. Animal Conservation 10.1111/acv.12227 ft025
es052 p020 c032 s035 Desertas petrel Pterodroma deserta Seabird B 5 2.20 ICC 0.6800 N NA yes GLS Arrival_breed Bugio Island, Madeira Europe -16.480000 32.43000 Breeding Table 2, p. 146 text Conservation implications of consistent foraging and trophic ecology in a rare petrel species 2016 Ramirez, I. and Paiva, V. H. and Fagundes, I. and Menezes, D. and Silva, I. and Ceia, F. R. and Phillips, R. A. and Ramos, J. A. and Garthe, S. Animal Conservation 10.1111/acv.12227 ft025
es053 p020 c033 s035 Desertas petrel Pterodroma deserta Seabird B 7 1.70 ICC 0.9000 N NA yes GLS Depart_breed Bugio Island, Madeira Europe -16.480000 32.43000 Breeding Table 2, p. 146 text Conservation implications of consistent foraging and trophic ecology in a rare petrel species 2016 Ramirez, I. and Paiva, V. H. and Fagundes, I. and Menezes, D. and Silva, I. and Ceia, F. R. and Phillips, R. A. and Ramos, J. A. and Garthe, S. Animal Conservation 10.1111/acv.12227 ft025
es054 p020 c034 s035 Desertas petrel Pterodroma deserta Seabird B 4 2.00 ICC 0.9100 N NA yes GLS Depart_breed Bugio Island, Madeira Europe -16.480000 32.43000 Breeding Table 2, p. 146 text Conservation implications of consistent foraging and trophic ecology in a rare petrel species 2016 Ramirez, I. and Paiva, V. H. and Fagundes, I. and Menezes, D. and Silva, I. and Ceia, F. R. and Phillips, R. A. and Ramos, J. A. and Garthe, S. Animal Conservation 10.1111/acv.12227 ft025
es055 p020 c033 s035 Desertas petrel Pterodroma deserta Seabird B 7 1.70 ICC 0.9200 N NA yes GLS Arrival_breed Bugio Island, Madeira Europe -16.480000 32.43000 Breeding Table 2, p. 146 text Conservation implications of consistent foraging and trophic ecology in a rare petrel species 2016 Ramirez, I. and Paiva, V. H. and Fagundes, I. and Menezes, D. and Silva, I. and Ceia, F. R. and Phillips, R. A. and Ramos, J. A. and Garthe, S. Animal Conservation 10.1111/acv.12227 ft025
es056 p020 c034 s035 Desertas petrel Pterodroma deserta Seabird B 4 2.00 ICC 0.9500 N NA yes GLS Arrival_breed Bugio Island, Madeira Europe -16.480000 32.43000 Breeding Table 2, p. 146 text Conservation implications of consistent foraging and trophic ecology in a rare petrel species 2016 Ramirez, I. and Paiva, V. H. and Fagundes, I. and Menezes, D. and Silva, I. and Ceia, F. R. and Phillips, R. A. and Ramos, J. A. and Garthe, S. Animal Conservation 10.1111/acv.12227 ft025
es057 p021 c035 s030 Dusky warbler Phylloscopus fuscatus Landbird M 12 2.00 ICC 0.3400 N NA no Conventional Arrival_breed Malkachan river, Russian Far East Europe 154.230000 59.85000 Breeding p. 6, text, results text Benefits of early arrival at breeding grounds vary between males 2002 Forstmeier, W. Journal of Animal Ecology 10.1046/j.0021-8790.2001.00569.x ft018
es058 p022 c036 s043 Eastern kingbird Tyrannus tyrannus Landbird M 30 2.50 ICC 0.0500 N NA no Conventional Arrival_breed Malheur National Wildlife Refuge, Oregon North America -118.900000 42.81600 Breeding p. 38, text, results text Age- and sex-dependent spring arrival dates of Eastern Kingbirds 2009 Cooper, N. W. and Murphy, M. T. and Redmond, L. J. Journal of Field Ornithology 10.1111/j.1557-9263.2009.00203.x ft006
es059 p022 c037 s043 Eastern kingbird Tyrannus tyrannus Landbird M 20 2.30 ICC 0.0500 N NA no Conventional Arrival_breed Malheur National Wildlife Refuge, Oregon North America -118.900000 42.81600 Breeding p. 38, text, results text Age- and sex-dependent spring arrival dates of Eastern Kingbirds 2009 Cooper, N. W. and Murphy, M. T. and Redmond, L. J. Journal of Field Ornithology 10.1111/j.1557-9263.2009.00203.x ft006
es060 p022 c038 s043 Eastern kingbird Tyrannus tyrannus Landbird F 26 2.30 ICC 0.2300 N NA no Conventional Arrival_breed Malheur National Wildlife Refuge, Oregon North America -118.900000 42.81600 Breeding p. 38, text, results text Age- and sex-dependent spring arrival dates of Eastern Kingbirds 2009 Cooper, N. W. and Murphy, M. T. and Redmond, L. J. Journal of Field Ornithology 10.1111/j.1557-9263.2009.00203.x ft006
es061 p022 c039 s043 Eastern kingbird Tyrannus tyrannus Landbird F 19 2.40 ICC 0.3900 N NA no Conventional Arrival_breed Malheur National Wildlife Refuge, Oregon North America -118.900000 42.81600 Breeding p. 38, text, results text Age- and sex-dependent spring arrival dates of Eastern Kingbirds 2009 Cooper, N. W. and Murphy, M. T. and Redmond, L. J. Journal of Field Ornithology 10.1111/j.1557-9263.2009.00203.x ft006
es062 p023 c040 s026 Egyptian vulture Neophron percnopterus Landbird B 6 3.80 ICC 0.4340 N NA no Satellite Nonbreed_depart Castell<f3>n and Guadalajara provinces (Spain) Europe -1.443056 40.43284 Breeding Table 2, p. 647 text Individual repeatability in timing and spatial flexibility of migration routes of trans-Saharan migratory raptors 2014 Lopez-Lopez, P. and Garcia-Ripolles, C. and Urios, V. Current Zoology 10.1093/czoolo/60.5.642 ft075
es063 p023 c040 s026 Egyptian vulture Neophron percnopterus Landbird B 6 3.80 ICC 0.5630 N NA no Satellite Arrival_breed Castell<f3>n and Guadalajara provinces (Spain) Europe -1.443056 40.43284 Breeding Table 2, p. 647 text Individual repeatability in timing and spatial flexibility of migration routes of trans-Saharan migratory raptors 2014 Lopez-Lopez, P. and Garcia-Ripolles, C. and Urios, V. Current Zoology 10.1093/czoolo/60.5.642 ft075
es064 p023 c040 s026 Egyptian vulture Neophron percnopterus Landbird B 6 4.20 ICC 0.7040 N NA no Satellite Nonbreed_arrival Castell<f3>n and Guadalajara provinces (Spain) Europe -1.443056 40.43284 Breeding Table 2, p. 647 text Individual repeatability in timing and spatial flexibility of migration routes of trans-Saharan migratory raptors 2014 Lopez-Lopez, P. and Garcia-Ripolles, C. and Urios, V. Current Zoology 10.1093/czoolo/60.5.642 ft075
es065 p023 c040 s026 Egyptian vulture Neophron percnopterus Landbird B 6 4.20 ICC 0.7050 N NA no Satellite Depart_breed Castell<f3>n and Guadalajara provinces (Spain) Europe -1.443056 40.43284 Breeding Table 2, p. 647 text Individual repeatability in timing and spatial flexibility of migration routes of trans-Saharan migratory raptors 2014 Lopez-Lopez, P. and Garcia-Ripolles, C. and Urios, V. Current Zoology 10.1093/czoolo/60.5.642 ft075
es066 p024 c041 s009 Emperor goose Chen canagicus Waterbird F 18 2.00 ICC 0.3410 N NA no Conventional Arrival_breed Kokechik Bay, YKD, Alaska North America -165.730000 61.67000 Breeding Table 2, p. 387 text Reproductive Ecology of Emperor Geese - Annual and Individual Variation in Nesting 1992 Petersen, M. R. Condor 10.2307/1369211 ft122
es067 p025 c042 s038 Eurasian woodcock Scolopax rusticola Waterbird B 5 2.80 ICC 0.1300 N NA yes Satellite Nonbreed_depart Italian peninsula Italy 14.260000 41.53000 Non-breeding Table 5, p. 161 text Interindividual variation and consistency of migratory behavior in the Eurasian woodcock 2020 Tedeschi, A. and Sorrenti, M. and Bottazzo, M. and Spagnesi, M. and Telletxea, I. and Ibanez, R. and Tormen, N. and De Pascalis, F. and Guidolin, L. and Rubolini, D. Current Zoology 10.1093/cz/zoz038 ft087
es068 p025 c042 s038 Eurasian woodcock Scolopax rusticola Waterbird B 8 2.60 ICC 0.8900 N NA yes Satellite Arrival_breed Italian peninsula Italy 14.260000 41.53000 Non-breeding Table 5, p. 161 text Interindividual variation and consistency of migratory behavior in the Eurasian woodcock 2020 Tedeschi, A. and Sorrenti, M. and Bottazzo, M. and Spagnesi, M. and Telletxea, I. and Ibanez, R. and Tormen, N. and De Pascalis, F. and Guidolin, L. and Rubolini, D. Current Zoology 10.1093/cz/zoz038 ft087
es069 p026 c043 s005 Ferruginous hawk Buteo regalis Landbird B 25 2.70 ICC 0.1700 N NA no Satellite Depart_breed Pacific northwest, northern grasslands, northern plains, Mexico (winter) North America -121.540000 47.48000 Both but mainly breeding grounds Table 3, p. 566 text Repeatability in migration of Ferruginous Hawks (Buteo regalis) and implications for nomadism 2019 Watson, J. W. and Keren, I. N. Wilson Journal of Ornithology 10.1676/18-171 ft118
es070 p026 c043 s005 Ferruginous hawk Buteo regalis Landbird B 18 2.60 ICC 0.2900 N NA no Satellite Nonbreed_arrival Pacific northwest, northern grasslands, northern plains, Mexico (winter) North America -121.540000 47.48000 Both but mainly breeding grounds Table 3, p. 566 text Repeatability in migration of Ferruginous Hawks (Buteo regalis) and implications for nomadism 2019 Watson, J. W. and Keren, I. N. Wilson Journal of Ornithology 10.1676/18-171 ft118
es071 p026 c043 s005 Ferruginous hawk Buteo regalis Landbird B 14 2.70 ICC 0.3400 N NA no Satellite Arrival_breed Pacific northwest, northern grasslands, northern plains, Mexico (winter) North America -121.540000 47.48000 Both but mainly breeding grounds Table 3, p. 566 text Repeatability in migration of Ferruginous Hawks (Buteo regalis) and implications for nomadism 2019 Watson, J. W. and Keren, I. N. Wilson Journal of Ornithology 10.1676/18-171 ft118
es072 p026 c043 s005 Ferruginous hawk Buteo regalis Landbird B 10 2.60 ICC 0.5300 N NA no Satellite Nonbreed_depart Pacific northwest, northern grasslands, northern plains, Mexico (winter) North America -121.540000 47.48000 Both but mainly breeding grounds Table 3, p. 566 text Repeatability in migration of Ferruginous Hawks (Buteo regalis) and implications for nomadism 2019 Watson, J. W. and Keren, I. N. Wilson Journal of Ornithology 10.1676/18-171 ft118
es073 p027 c044 s001 Great reed warbler Acrocephalus arundinaceus Landbird M 7 2.30 ICC -0.3600 N NA no GLS Depart_breed Lake Kvismaren, Sweden Europe 15.400516 59.16956 Breeding p. 96, text, results text Individual consistency of long-distance migration in a songbird: significant repeatability of autumn route, stopovers and wintering sites but not in timing of migration 2017 Hasselquist, D. and Montras-Janer, T. and Tarka, M. and Hansson, B. Journal of Avian Biology 10.1111/jav.01292 ft073
es074 p027 c044 s001 Great reed warbler Acrocephalus arundinaceus Landbird M 4 2.30 ICC 0.2300 N NA no GLS Nonbreed_depart Lake Kvismaren, Sweden Europe 15.400516 59.16956 Breeding p. 96, text, results text Individual consistency of long-distance migration in a songbird: significant repeatability of autumn route, stopovers and wintering sites but not in timing of migration 2017 Hasselquist, D. and Montras-Janer, T. and Tarka, M. and Hansson, B. Journal of Avian Biology 10.1111/jav.01292 ft073
es075 p027 c044 s001 Great reed warbler Acrocephalus arundinaceus Landbird M 4 2.30 ICC 0.2500 N NA no GLS Arrival_breed Lake Kvismaren, Sweden Europe 15.400516 59.16956 Breeding p. 97, text, results text Individual consistency of long-distance migration in a songbird: significant repeatability of autumn route, stopovers and wintering sites but not in timing of migration 2017 Hasselquist, D. and Montras-Janer, T. and Tarka, M. and Hansson, B. Journal of Avian Biology 10.1111/jav.01292 ft073
es076 p028 c045 s002 Greater snow goose Anser caerulescens atlanticus Waterbird B 20 2.10 ICC 0.4200 N NA no Conventional Arrival_breed Bylot Island migratory bird sanctuary, Canada North America -78.490000 73.21000 both? Table 2, p. 4 text Individual variation in timing of migration: causes and reproductive consequences in greater snow geese (Anser caerulescens atlanticus) 2004 Bety, J. and Giroux, J. F. and Gauthier, G. Behavioral Ecology and Sociobiology 10.1007/s00265-004-0840-3 ft080
es077 p029 c046 s032 Grey petrel Procellaria cinerea Seabird B 7 4.30 ICC 0.4100 N NA no GLS Arrival_breed Mayes Island, Kerguelen Islands Not 69.940000 -49.47000 Breeding Table 3, p. 99 text Individual Consistency in the Non-Breeding Behavior of a Long-Distance Migrant Seabird, the Grey Petrel Procellaria Cinerea 2019 Delord, K. and Barbraud, C. and Pinaud, D. and Ruault, S. and Patrick, S. C. and Weimerskirch, H. Marine Ornithology ft072
es078 p029 c046 s032 Grey petrel Procellaria cinerea Seabird B 7 4.30 ICC 0.4400 N NA no GLS Depart_breed Mayes Island, Kerguelen Islands Not 69.940000 -49.47000 Breeding Table 3, p. 99 text Individual Consistency in the Non-Breeding Behavior of a Long-Distance Migrant Seabird, the Grey Petrel Procellaria Cinerea 2019 Delord, K. and Barbraud, C. and Pinaud, D. and Ruault, S. and Patrick, S. C. and Weimerskirch, H. Marine Ornithology ft072
es079 p029 c046 s032 Grey petrel Procellaria cinerea Seabird B 7 4.30 ICC 0.5500 N NA no GLS Nonbreed_arrival Mayes Island, Kerguelen Islands Not 69.940000 -49.47000 Breeding Table 3, p. 99 text Individual Consistency in the Non-Breeding Behavior of a Long-Distance Migrant Seabird, the Grey Petrel Procellaria Cinerea 2019 Delord, K. and Barbraud, C. and Pinaud, D. and Ruault, S. and Patrick, S. C. and Weimerskirch, H. Marine Ornithology ft072
es080 p029 c046 s032 Grey petrel Procellaria cinerea Seabird B 7 4.30 ICC 0.7000 N NA no GLS Nonbreed_depart Mayes Island, Kerguelen Islands Not 69.940000 -49.47000 Breeding Table 3, p. 99 text Individual Consistency in the Non-Breeding Behavior of a Long-Distance Migrant Seabird, the Grey Petrel Procellaria Cinerea 2019 Delord, K. and Barbraud, C. and Pinaud, D. and Ruault, S. and Patrick, S. C. and Weimerskirch, H. Marine Ornithology ft072
es081 p030 c047 s044 Hoopoe Upupa epops Landbird B 16 2.00 ICC 0.2400 N NA no GLS Nonbreed_depart southern Switzerland Europe 7.360000 46.23000 Breeding Table 2, p. 8683 text Repeatability of individual migration routes, wintering sites, and timing in a long-distance migrant bird 2016 van Wijk, R. E. and Bauer, S. and Schaub, M. Ecology and Evolution 10.1002/ece3.2578 ft120
es082 p030 c047 s044 Hoopoe Upupa epops Landbird B 12 2.00 ICC 0.4300 N NA no GLS Arrival_breed southern Switzerland Europe 7.360000 46.23000 Breeding Table 2, p. 8683 text Repeatability of individual migration routes, wintering sites, and timing in a long-distance migrant bird 2016 van Wijk, R. E. and Bauer, S. and Schaub, M. Ecology and Evolution 10.1002/ece3.2578 ft120
es083 p030 c047 s044 Hoopoe Upupa epops Landbird B 16 2.00 ICC 0.7300 N NA no GLS Nonbreed_arrival southern Switzerland Europe 7.360000 46.23000 Breeding Table 2, p. 8683 text Repeatability of individual migration routes, wintering sites, and timing in a long-distance migrant bird 2016 van Wijk, R. E. and Bauer, S. and Schaub, M. Ecology and Evolution 10.1002/ece3.2578 ft120
es084 p030 c047 s044 Hoopoe Upupa epops Landbird B 14 2.00 ICC 0.7500 N NA no GLS Depart_breed southern Switzerland Europe 7.360000 46.23000 Breeding Table 2, p. 8683 text Repeatability of individual migration routes, wintering sites, and timing in a long-distance migrant bird 2016 van Wijk, R. E. and Bauer, S. and Schaub, M. Ecology and Evolution 10.1002/ece3.2578 ft120
es085 p031 c048 s022 Marbled godwit Limosa fedoa Waterbird B 5 3.20 ICC 0.0000 N NA yes Satellite Arrival_breed Ugashik Bay, Alaska North America -157.390000 57.51000 Breeding Figure 3. C, p. 6 figure Flexible timing of annual movements across consistently used sites by Marbled Godwits breeding in Alaska 2019 Ruthrauff, D. R. and Tibbitts, T. L. and Gill, R. E. Auk 10.1093/auk/uky007 ft054
es086 p031 c048 s022 Marbled godwit Limosa fedoa Waterbird B 5 3.20 ICC 0.2500 N NA yes Satellite Nonbreed_depart Ugashik Bay, Alaska North America -157.390000 57.51000 Breeding Figure 3. C, p. 6 figure Flexible timing of annual movements across consistently used sites by Marbled Godwits breeding in Alaska 2019 Ruthrauff, D. R. and Tibbitts, T. L. and Gill, R. E. Auk 10.1093/auk/uky007 ft054
es087 p031 c048 s022 Marbled godwit Limosa fedoa Waterbird B 5 4.00 ICC 0.3200 N NA yes Satellite Depart_breed Ugashik Bay, Alaska North America -157.390000 57.51000 Breeding Figure 3. C, p. 6 figure Flexible timing of annual movements across consistently used sites by Marbled Godwits breeding in Alaska 2019 Ruthrauff, D. R. and Tibbitts, T. L. and Gill, R. E. Auk 10.1093/auk/uky007 ft054
es088 p031 c048 s022 Marbled godwit Limosa fedoa Waterbird B 5 4.00 ICC 0.4500 N NA yes Satellite Nonbreed_arrival Ugashik Bay, Alaska North America -157.390000 57.51000 Breeding Figure 3. C, p. 6 figure Flexible timing of annual movements across consistently used sites by Marbled Godwits breeding in Alaska 2019 Ruthrauff, D. R. and Tibbitts, T. L. and Gill, R. E. Auk 10.1093/auk/uky007 ft054
es089 p032 c049 s011 Marsh harrier Circus aeruginosus Landbird B 4 4.80 ICC 0.3500 N NA yes Satellite Depart_breed Bergslagen, Lindesberg, Sweden Europe 15.161838 58.79720 Breeding Table 3, p. 181 text Consistency in long-distance bird migration: contrasting patterns in time and space for two raptors 2016 Vardanis, Y. and Nilsson, J. A. and Klaassen, R. H. G. and Strandberg, R. and Alerstam, T. Animal Behaviour 10.1016/j.anbehav.2015.12.014 ft026
es090 p032 c049 s011 Marsh harrier Circus aeruginosus Landbird B 4 4.80 ICC 0.6000 N NA yes Satellite Nonbreed_arrival Bergslagen, Lindesberg, Sweden Europe 15.161838 58.79720 Breeding Table 3, p. 181 text Consistency in long-distance bird migration: contrasting patterns in time and space for two raptors 2016 Vardanis, Y. and Nilsson, J. A. and Klaassen, R. H. G. and Strandberg, R. and Alerstam, T. Animal Behaviour 10.1016/j.anbehav.2015.12.014 ft026
es091 p032 c049 s011 Marsh harrier Circus aeruginosus Landbird B 3 5.00 ICC 0.6300 N NA yes Satellite Arrival_breed Bergslagen, Lindesberg, Sweden Europe 15.161838 58.79720 Breeding Table 3, p. 181 text Consistency in long-distance bird migration: contrasting patterns in time and space for two raptors 2016 Vardanis, Y. and Nilsson, J. A. and Klaassen, R. H. G. and Strandberg, R. and Alerstam, T. Animal Behaviour 10.1016/j.anbehav.2015.12.014 ft026
es092 p032 c049 s011 Marsh harrier Circus aeruginosus Landbird B 3 5.00 ICC 0.8100 N NA yes Satellite Nonbreed_depart Bergslagen, Lindesberg, Sweden Europe 15.161838 58.79720 Breeding Table 3, p. 181 text Consistency in long-distance bird migration: contrasting patterns in time and space for two raptors 2016 Vardanis, Y. and Nilsson, J. A. and Klaassen, R. H. G. and Strandberg, R. and Alerstam, T. Animal Behaviour 10.1016/j.anbehav.2015.12.014 ft026
es093 p033 c050 s025 Northern gannet Morus bassanus Seabird B 16 2.00 ICC 0.0100 N NA no GLS Depart_breed Bonaventure Island North America -64.150000 48.48300 Breeding Table 5, p. 29 text Migratory tactics and wintering areas of northern gannets (morus bassanus) breeding in North America 2014 Fifield, D. A. and Montevecchi, W. A. and Garthe, S. and Robertson, G. J. and Kubetzki, U. and Rail, J. F. Ornithological Monographs ft109
es094 p033 c050 s025 Northern gannet Morus bassanus Seabird B 14 2.00 ICC 0.0100 N NA no GLS Arrival_breed Bonaventure Island North America -64.150000 48.48300 Breeding Table 5, p. 29 text Migratory tactics and wintering areas of northern gannets (morus bassanus) breeding in North America 2014 Fifield, D. A. and Montevecchi, W. A. and Garthe, S. and Robertson, G. J. and Kubetzki, U. and Rail, J. F. Ornithological Monographs ft109
es095 p033 c050 s025 Northern gannet Morus bassanus Seabird B 16 2.00 ICC 0.4200 N NA no GLS Nonbreed_arrival Bonaventure Island North America -64.150000 48.48300 Breeding Table 5, p. 29 text Migratory tactics and wintering areas of northern gannets (morus bassanus) breeding in North America 2014 Fifield, D. A. and Montevecchi, W. A. and Garthe, S. and Robertson, G. J. and Kubetzki, U. and Rail, J. F. Ornithological Monographs ft109
es096 p033 c050 s025 Northern gannet Morus bassanus Seabird B 14 2.00 ICC 0.9000 N NA no GLS Nonbreed_depart Bonaventure Island North America -64.150000 48.48300 Breeding Table 5, p. 29 text Migratory tactics and wintering areas of northern gannets (morus bassanus) breeding in North America 2014 Fifield, D. A. and Montevecchi, W. A. and Garthe, S. and Robertson, G. J. and Kubetzki, U. and Rail, J. F. Ornithological Monographs ft109
es097 p032 c051 s028 Osprey Pandion haliaetus Landbird B 7 2.90 ICC 0.0400 N NA yes Satellite Nonbreed_arrival Bergslagen, Lindesberg, Sweden Europe 15.161838 58.79720 Breeding Table 3, p. 181 text Consistency in long-distance bird migration: contrasting patterns in time and space for two raptors 2016 Vardanis, Y. and Nilsson, J. A. and Klaassen, R. H. G. and Strandberg, R. and Alerstam, T. Animal Behaviour 10.1016/j.anbehav.2015.12.014 ft026
es098 p032 c051 s028 Osprey Pandion haliaetus Landbird B 4 3.30 ICC 0.0700 N NA yes Satellite Arrival_breed Bergslagen, Lindesberg, Sweden Europe 15.161838 58.79720 Breeding Table 3, p. 181 text Consistency in long-distance bird migration: contrasting patterns in time and space for two raptors 2016 Vardanis, Y. and Nilsson, J. A. and Klaassen, R. H. G. and Strandberg, R. and Alerstam, T. Animal Behaviour 10.1016/j.anbehav.2015.12.014 ft026
es099 p032 c051 s028 Osprey Pandion haliaetus Landbird B 7 2.90 ICC 0.1700 N NA yes Satellite Depart_breed Bergslagen, Lindesberg, Sweden Europe 15.161838 58.79720 Breeding Table 3, p. 181 text Consistency in long-distance bird migration: contrasting patterns in time and space for two raptors 2016 Vardanis, Y. and Nilsson, J. A. and Klaassen, R. H. G. and Strandberg, R. and Alerstam, T. Animal Behaviour 10.1016/j.anbehav.2015.12.014 ft026
es100 p032 c051 s028 Osprey Pandion haliaetus Landbird B 4 3.30 ICC 0.3800 N NA yes Satellite Nonbreed_depart Bergslagen, Lindesberg, Sweden Europe 15.161838 58.79720 Breeding Table 3, p. 181 text Consistency in long-distance bird migration: contrasting patterns in time and space for two raptors 2016 Vardanis, Y. and Nilsson, J. A. and Klaassen, R. H. G. and Strandberg, R. and Alerstam, T. Animal Behaviour 10.1016/j.anbehav.2015.12.014 ft026
es101 p034 c052 s020 Pallas gull Larus ichthyaetus Seabird B 4 3.00 ICC -0.0700 N NA no Satellite Depart_breed Qinghai Lake, China Not 100.000000 36.88000 Breeding Table 2, p. 10 text Detours in long-distance migration across the Qinghai-Tibetan Plateau: Individual consistency and habitat associations 2018 Liu, D. and Zhang, G. and Jiang, H. and Lu, J. PeerJ 10.7717/peerj.4304 ft035
es102 p034 c052 s020 Pallas gull Larus ichthyaetus Seabird B 4 2.50 ICC 0.7800 N NA no Satellite Arrival_breed Qinghai Lake, China Not 100.000000 36.88000 Breeding Table 2, p. 10 text Detours in long-distance migration across the Qinghai-Tibetan Plateau: Individual consistency and habitat associations 2018 Liu, D. and Zhang, G. and Jiang, H. and Lu, J. PeerJ 10.7717/peerj.4304 ft035
es103 p034 c052 s020 Pallas gull Larus ichthyaetus Seabird B 4 2.50 ICC 0.8400 N NA no Satellite Nonbreed_depart Qinghai Lake, China Not 100.000000 36.88000 Breeding Table 2, p. 10 text Detours in long-distance migration across the Qinghai-Tibetan Plateau: Individual consistency and habitat associations 2018 Liu, D. and Zhang, G. and Jiang, H. and Lu, J. PeerJ 10.7717/peerj.4304 ft035
es104 p034 c052 s020 Pallas gull Larus ichthyaetus Seabird B 4 3.00 ICC 0.8500 N NA no Satellite Nonbreed_arrival Qinghai Lake, China Not 100.000000 36.88000 Breeding Table 2, p. 10 text Detours in long-distance migration across the Qinghai-Tibetan Plateau: Individual consistency and habitat associations 2018 Liu, D. and Zhang, G. and Jiang, H. and Lu, J. PeerJ 10.7717/peerj.4304 ft035
es105 p035 c053 s014 Pied flycatcher Ficedula hypoleuca Landbird F 116 2.30 ICC 0.0780 Y Age no Conventional Arrival_breed Sinober in S<f8>rkedalen Europe 10.633000 59.98300 Breeding p. 992, text, results text Advancement of spring arrival in a long-term study of a passerine bird: sex, age and environmental effects 2017 Cadahia, L. and Labra, A. and Knudsen, E. and Nilsson, A. and Lampe, H. M. and Slagsvold, T. and Stenseth, N. C. Oecologia 10.1007/s00442-017-3922-4 ft002
es106 p036 c056 s014 Pied flycatcher Ficedula hypoleuca Landbird M 39 2.30 ICC 0.0900 N NA Conventional Arrival_breed La Hiruela, Spain Europe -3.450000 41.06600 Breeding p. 704, text, results text Arrival time from spring migration in male Pied Flycatchers: Individual consistency and familial resemblance 1998 Potti, J. Condor 10.2307/1369752 ft016
es107 p035 c054 s014 Pied flycatcher Ficedula hypoleuca Landbird M 364 2.70 ICC 0.1670 Y Age no Conventional Arrival_breed Sinober in S<f8>rkedalen Europe 10.633000 59.98300 Breeding p. 992, text, results text Advancement of spring arrival in a long-term study of a passerine bird: sex, age and environmental effects 2017 Cadahia, L. and Labra, A. and Knudsen, E. and Nilsson, A. and Lampe, H. M. and Slagsvold, T. and Stenseth, N. C. Oecologia 10.1007/s00442-017-3922-4 ft002
es108 p035 c055 s014 Pied flycatcher Ficedula hypoleuca Landbird B 480 2.60 ICC 0.2120 Y Age no Conventional Arrival_breed Sinober in S<f8>rkedalen Europe 10.633000 59.98300 Breeding p. 992, text, results text Advancement of spring arrival in a long-term study of a passerine bird: sex, age and environmental effects 2017 Cadahia, L. and Labra, A. and Knudsen, E. and Nilsson, A. and Lampe, H. M. and Slagsvold, T. and Stenseth, N. C. Oecologia 10.1007/s00442-017-3922-4 ft002
es109 p037 c057 s014 Pied flycatcher Ficedula hypoleuca Landbird M 307 2.31 ICC 0.2700 N NA no Conventional Arrival_breed Drenthe, The Netherlands Europe 6.360000 52.81600 Breeding Table 3, p. 14 text Repeatability in spring arrival dates in Pied Flycatchers varies among years and sexes 2016 Both, C. and Bijlsma, R. G. and Ouwehand, J. Ardea 10.5253/arde.v104i1.a1 ft119
es110 p037 c058 s014 Pied flycatcher Ficedula hypoleuca Landbird F 221 2.39 ICC 0.3000 N NA no Conventional Arrival_breed Drenthe, The Netherlands Europe 6.360000 52.81600 Breeding Table 3, p. 14 text Repeatability in spring arrival dates in Pied Flycatchers varies among years and sexes 2016 Both, C. and Bijlsma, R. G. and Ouwehand, J. Ardea 10.5253/arde.v104i1.a1 ft119
es111 p038 c059 s033 Purple martin Progne subis Landbird B 33 2.00 ICC 0.0009 Y Sex, age (fixed), breeding colony, individual, year (random) no GLS Nonbreed_arrival US -80.090000 41.80000 Breeding Table 1, p. 3 text Individual Variability in Migration Timing Can Explain Long-Term, Population-Level Advances in a Songbird 2019 Fraser, K. C. and Shave, A. and de Greef, E. and Siegrist, J. and Garroway, C. J. Frontiers in Ecology and Evolution 10.3389/fevo.2019.00324 ft077
es112 p038 c059 s033 Purple martin Progne subis Landbird B 33 2.00 ICC 0.0010 Y Sex, age (fixed), breeding colony, individual, year (random) no GLS Depart_breed US -80.090000 41.80000 Breeding Table 1, p. 3 text Individual Variability in Migration Timing Can Explain Long-Term, Population-Level Advances in a Songbird 2019 Fraser, K. C. and Shave, A. and de Greef, E. and Siegrist, J. and Garroway, C. J. Frontiers in Ecology and Evolution 10.3389/fevo.2019.00324 ft077
es113 p038 c059 s033 Purple martin Progne subis Landbird B 33 2.00 ICC 0.3200 Y Sex, age (fixed), breeding colony, individual, year (random) no GLS Arrival_breed US -80.090000 41.80000 Breeding Table 1, p. 3 text Individual Variability in Migration Timing Can Explain Long-Term, Population-Level Advances in a Songbird 2019 Fraser, K. C. and Shave, A. and de Greef, E. and Siegrist, J. and Garroway, C. J. Frontiers in Ecology and Evolution 10.3389/fevo.2019.00324 ft077
es114 p038 c059 s033 Purple martin Progne subis Landbird B 33 2.00 ICC 0.3900 Y Sex, age (fixed), breeding colony, individual, year (random) no GLS Nonbreed_depart US -80.090000 41.80000 Breeding Table 1, p. 3 text Individual Variability in Migration Timing Can Explain Long-Term, Population-Level Advances in a Songbird 2019 Fraser, K. C. and Shave, A. and de Greef, E. and Siegrist, J. and Garroway, C. J. Frontiers in Ecology and Evolution 10.3389/fevo.2019.00324 ft077
es115 p039 c060 s018 Red-backed shrike Lanius collurio Landbird M 7 2.00 ICC 0.3800 N NA no GLS Depart_breed Gribskov, Denmark Europe 12.281090 55.96581 Breeding Table 1, p. 139 text Full-year tracking suggests endogenous control of migration timing in a long-distance migratory songbird 2018 Pedersen, L. and Jackson, K. and Thorup, K. and Tottrup, A. P. Behavioral Ecology and Sociobiology 10.1007/s00265-018-2553-z ft055
es116 p040 c061 s034 Round Island petrel Pterodroma arminjoniana Seabird B 76 2.20 ICC 0.7870 N NA yes GLS Depart_breed Round Island Mauritius 57.780000 -19.85000 Breeding NA NA Individual consistency in migration strategies of a tropical seabird, the Round Island petrel 2022 Franklin, K. A. and Norris, K. and Gill, J. A. and Ratcliffe, N. and Bonnet-Lebrun A-S. and Butler, S. J. and Cole. N. C. and Jones, C. G. and Lisovski, S. and Ruhomaun, K. and Tatayah, V. Nicoll, M. A. C. Movement Ecology NA NA
es117 p040 c061 s034 Round Island petrel Pterodroma arminjoniana Seabird B 62 2.10 ICC 0.8130 N NA yes GLS Arrival_breed Round Island Mauritius 57.780000 -19.85000 Breeding NA NA Individual consistency in migration strategies of a tropical seabird, the Round Island petrel 2022 Franklin, K. A. and Norris, K. and Gill, J. A. and Ratcliffe, N. and Bonnet-Lebrun A-S. and Butler, S. J. and Cole. N. C. and Jones, C. G. and Lisovski, S. and Ruhomaun, K. and Tatayah, V. Nicoll, M. A. C. Movement Ecology MA NA
es118 p041 c062 s006 Scopoli’s shearwater Calonectris diomedea Seabird F 5 2.20 ICC 0.0000 N NA no GLS Arrival_breed Linosa, Sicily Europe 12.868000 35.86400 Breeding Table 3, p. 635 text Individual consistency and sex differences in migration strategies of Scopoli’s shearwaters Calonectris diomedea despite year differences 2014 Muller, M. S. and Massa, B. and Phillips, R. A. and Dell’Omo, G. Current Zoology 10.1093/czoolo/60.5.631 ft069
es119 p041 c063 s006 Scopoli’s shearwater Calonectris diomedea Seabird M 7 2.10 ICC 0.1990 N NA no GLS Arrival_breed Linosa, Sicily Europe 12.868000 35.86400 Breeding Unsure if they have fixed effect Table 3, p. 635 text Individual consistency and sex differences in migration strategies of Scopoli’s shearwaters Calonectris diomedea despite year differences 2014 Muller, M. S. and Massa, B. and Phillips, R. A. and Dell’Omo, G. Current Zoology 10.1093/czoolo/60.5.631 ft069
es120 p041 c062 s006 Scopoli’s shearwater Calonectris diomedea Seabird F 5 2.20 ICC 0.3560 N NA no GLS Depart_breed Linosa, Sicily Europe 12.868000 35.86400 Breeding Table 3, p. 635 text Individual consistency and sex differences in migration strategies of Scopoli’s shearwaters Calonectris diomedea despite year differences 2014 Muller, M. S. and Massa, B. and Phillips, R. A. and Dell’Omo, G. Current Zoology 10.1093/czoolo/60.5.631 ft069
es121 p041 c063 s006 Scopoli’s shearwater Calonectris diomedea Seabird M 7 2.10 ICC 0.4160 N NA no GLS Depart_breed Linosa, Sicily Europe 12.868000 35.86400 Breeding Table 3, p. 635 text Individual consistency and sex differences in migration strategies of Scopoli’s shearwaters Calonectris diomedea despite year differences 2014 Muller, M. S. and Massa, B. and Phillips, R. A. and Dell’Omo, G. Current Zoology 10.1093/czoolo/60.5.631 ft069
es122 p042 c064 s007 Streaked shearwater Calonectris leucomelas Seabird F 10 2.00 r -0.4800 N NA NA GLS Depart_breed Sangan Island, Mikura Island and Awa Island, Japan Not 141.980000 39.31000 Breeding Figure 3. A, p. 694 text Individual consistency in migratory behaviour of a pelagic seabird 2014 Yamamoto, T. and Takahashi, A. and Sato, K. and Oka, N. and Yamamoto, M. and Trathan, P. N. Behaviour 10.1163/1568539X-00003163 ft070
es123 p042 c065 s007 Streaked shearwater Calonectris leucomelas Seabird M 12 2.00 r -0.4100 N NA NA GLS Depart_breed Sangan Island, Mikura Island and Awa Island, Japan Not 141.980000 39.31000 Breeding Figure 3. A, p. 694 text Individual consistency in migratory behaviour of a pelagic seabird 2014 Yamamoto, T. and Takahashi, A. and Sato, K. and Oka, N. and Yamamoto, M. and Trathan, P. N. Behaviour 10.1163/1568539X-00003163 ft070
es124 p042 c066 s007 Streaked shearwater Calonectris leucomelas Seabird M 11 2.00 r -0.3000 N NA NA GLS Depart_breed Sangan Island, Mikura Island and Awa Island, Japan Not 141.980000 39.31000 Breeding Figure 3. A, p. 694 text Individual consistency in migratory behaviour of a pelagic seabird 2014 Yamamoto, T. and Takahashi, A. and Sato, K. and Oka, N. and Yamamoto, M. and Trathan, P. N. Behaviour 10.1163/1568539X-00003163 ft070
es125 p042 c065 s007 Streaked shearwater Calonectris leucomelas Seabird M 13 2.00 r -0.2400 N NA NA GLS Arrival_breed Sangan Island, Mikura Island and Awa Island, Japan Not 141.980000 39.31000 Breeding Figure 3. D, p. 694 text Individual consistency in migratory behaviour of a pelagic seabird 2014 Yamamoto, T. and Takahashi, A. and Sato, K. and Oka, N. and Yamamoto, M. and Trathan, P. N. Behaviour 10.1163/1568539X-00003163 ft070
es126 p042 c067 s007 Streaked shearwater Calonectris leucomelas Seabird F 11 2.00 r -0.1500 N NA NA GLS Depart_breed Sangan Island, Mikura Island and Awa Island, Japan Not 141.980000 39.31000 Breeding Figure 3. A, p. 694 text Individual consistency in migratory behaviour of a pelagic seabird 2014 Yamamoto, T. and Takahashi, A. and Sato, K. and Oka, N. and Yamamoto, M. and Trathan, P. N. Behaviour 10.1163/1568539X-00003163 ft070
es127 p042 c066 s007 Streaked shearwater Calonectris leucomelas Seabird M 9 2.00 r 0.0800 N NA NA GLS Arrival_breed Sangan Island, Mikura Island and Awa Island, Japan Not 141.980000 39.31000 Breeding Figure 3. D, p. 694 text Individual consistency in migratory behaviour of a pelagic seabird 2014 Yamamoto, T. and Takahashi, A. and Sato, K. and Oka, N. and Yamamoto, M. and Trathan, P. N. Behaviour 10.1163/1568539X-00003163 ft070
es128 p042 c064 s007 Streaked shearwater Calonectris leucomelas Seabird F 10 2.00 r 0.1800 N NA NA GLS Arrival_breed Sangan Island, Mikura Island and Awa Island, Japan Not 141.980000 39.31000 Breeding Figure 3. D, p. 694 text Individual consistency in migratory behaviour of a pelagic seabird 2014 Yamamoto, T. and Takahashi, A. and Sato, K. and Oka, N. and Yamamoto, M. and Trathan, P. N. Behaviour 10.1163/1568539X-00003163 ft070
es129 p042 c067 s007 Streaked shearwater Calonectris leucomelas Seabird F 9 2.00 r 0.9500 N NA NA GLS Arrival_breed Sangan Island, Mikura Island and Awa Island, Japan Not 141.980000 39.31000 Breeding Figure 3. D, p. 694 text Individual consistency in migratory behaviour of a pelagic seabird 2014 Yamamoto, T. and Takahashi, A. and Sato, K. and Oka, N. and Yamamoto, M. and Trathan, P. N. Behaviour 10.1163/1568539X-00003163 ft070
es130 p017 c024 s046 Thick-billed murre Uria lomvia Seabird B 3 1.30 ICC 0.0000 N NA no GLS Arrival_breed 7 seabird colonies ranging 74 to 47 N North America -82.970000 62.54000 Breeding Table 2, p. 7 text Individual winter movement strategies in two species of murre (Uria spp.) in the Northwest Atlantic 2014 McFarlane Tranquilla, L. A. and Montevecchi, W. A. and Fifield, D. A. and Hedd, A. and Gaston, A. J. and Robertson, G. J. and Phillips, R. A. PLoS ONE 10.1371/journal.pone.0090583 ft081
es131 p017 c025 s046 Thick-billed murre Uria lomvia Seabird B 8 2.10 ICC 0.0100 N NA no GLS Depart_breed 7 seabird colonies ranging 74 to 47 N North America -82.970000 62.54000 Breeding Table 2, p. 7 text Individual winter movement strategies in two species of murre (Uria spp.) in the Northwest Atlantic 2014 McFarlane Tranquilla, L. A. and Montevecchi, W. A. and Fifield, D. A. and Hedd, A. and Gaston, A. J. and Robertson, G. J. and Phillips, R. A. PLoS ONE 10.1371/journal.pone.0090583 ft081
es132 p017 c026 s046 Thick-billed murre Uria lomvia Seabird B 7 1.40 ICC 0.0100 N NA no GLS Depart_breed 7 seabird colonies ranging 74 to 47 N North America -82.970000 62.54000 Breeding Table 2, p. 7 text Individual winter movement strategies in two species of murre (Uria spp.) in the Northwest Atlantic 2014 McFarlane Tranquilla, L. A. and Montevecchi, W. A. and Fifield, D. A. and Hedd, A. and Gaston, A. J. and Robertson, G. J. and Phillips, R. A. PLoS ONE 10.1371/journal.pone.0090583 ft081
es133 p017 c024 s046 Thick-billed murre Uria lomvia Seabird B 3 2.00 ICC 0.0100 N NA no GLS Depart_breed 7 seabird colonies ranging 74 to 47 N North America -82.970000 62.54000 Breeding Table 2, p. 7 text Individual winter movement strategies in two species of murre (Uria spp.) in the Northwest Atlantic 2014 McFarlane Tranquilla, L. A. and Montevecchi, W. A. and Fifield, D. A. and Hedd, A. and Gaston, A. J. and Robertson, G. J. and Phillips, R. A. PLoS ONE 10.1371/journal.pone.0090583 ft081
es134 p017 c026 s046 Thick-billed murre Uria lomvia Seabird B 7 2.00 ICC 0.0400 N NA no GLS Arrival_breed 7 seabird colonies ranging 74 to 47 N North America -82.970000 62.54000 Breeding Table 2, p. 7 text Individual winter movement strategies in two species of murre (Uria spp.) in the Northwest Atlantic 2014 McFarlane Tranquilla, L. A. and Montevecchi, W. A. and Fifield, D. A. and Hedd, A. and Gaston, A. J. and Robertson, G. J. and Phillips, R. A. PLoS ONE 10.1371/journal.pone.0090583 ft081
es135 p017 c025 s046 Thick-billed murre Uria lomvia Seabird B 8 1.50 ICC 0.1800 N NA no GLS Arrival_breed 7 seabird colonies ranging 74 to 47 N North America -82.970000 62.54000 Breeding Table 2, p. 7 text Individual winter movement strategies in two species of murre (Uria spp.) in the Northwest Atlantic 2014 McFarlane Tranquilla, L. A. and Montevecchi, W. A. and Fifield, D. A. and Hedd, A. and Gaston, A. J. and Robertson, G. J. and Phillips, R. A. PLoS ONE 10.1371/journal.pone.0090583 ft081
es136 p043 c068 s027 Whimbrel Numenius phaeopus Waterbird B 12 2.60 ICC 0.2300 N NA no GLS Arrival_breed Iceland Europe -20.200000 63.80000 Breeding Table 2, p. 5 text Why Are Whimbrels Not Advancing Their Arrival Dates Into Iceland? Exploring Seasonal and Sex-Specific Variation in Consistency of Individual Timing During the Annual Cycle 2019 Carneiro, C. and Gunnarsson, T. G. and Alves, J. A. Frontiers in Ecology and Evolution 10.3389/fevo.2019.00248 ft149
es137 p043 c068 s027 Whimbrel Numenius phaeopus Waterbird B 16 2.90 ICC 0.2600 N NA no GLS Nonbreed_arrival Iceland Europe -20.200000 63.80000 Breeding Table 2, p. 5 text Why Are Whimbrels Not Advancing Their Arrival Dates Into Iceland? Exploring Seasonal and Sex-Specific Variation in Consistency of Individual Timing During the Annual Cycle 2019 Carneiro, C. and Gunnarsson, T. G. and Alves, J. A. Frontiers in Ecology and Evolution 10.3389/fevo.2019.00248 ft149
es138 p043 c068 s027 Whimbrel Numenius phaeopus Waterbird B 16 2.90 ICC 0.2800 N NA no GLS Depart_breed Iceland Europe -20.200000 63.80000 Breeding Table 2, p. 5 text Why Are Whimbrels Not Advancing Their Arrival Dates Into Iceland? Exploring Seasonal and Sex-Specific Variation in Consistency of Individual Timing During the Annual Cycle 2019 Carneiro, C. and Gunnarsson, T. G. and Alves, J. A. Frontiers in Ecology and Evolution 10.3389/fevo.2019.00248 ft149
es139 p043 c068 s027 Whimbrel Numenius phaeopus Waterbird B 12 2.60 ICC 0.7600 N NA no GLS Nonbreed_depart Iceland Europe -20.200000 63.80000 Breeding Table 2, p. 5 text Why Are Whimbrels Not Advancing Their Arrival Dates Into Iceland? Exploring Seasonal and Sex-Specific Variation in Consistency of Individual Timing During the Annual Cycle 2019 Carneiro, C. and Gunnarsson, T. G. and Alves, J. A. Frontiers in Ecology and Evolution 10.3389/fevo.2019.00248 ft149
es140 p044 c069 s010 White stork Ciconia ciconia Waterbird B 35 2.60 ICC 0.4900 N NA no Satellite Arrival_breed Saxony- Anhalt Germany 11.500000 51.90000 Breeding p. 1631, text, results text Early arrival at breeding grounds: Causes, costs and a trade-off with overwintering latitude 2018 Rotics, S. and Kaatz, M. and Turjeman, S. and Zurell, D. and Wikelski, M. and Sapir, N. and Eggers, U. and Fiedler, W. and Jeltsch, F. and Nathan, R. Journal of Animal Ecology 10.1111/1365-2656.12898 ft164
es141 p044 c069 s010 White stork Ciconia ciconia Waterbird B 35 2.60 ICC 0.5100 N NA no Satellite Nonbreed_depart Saxony- Anhalt Germany 11.500000 51.90000 Breeding p. 1631, text, results text Early arrival at breeding grounds: Causes, costs and a trade-off with overwintering latitude 2018 Rotics, S. and Kaatz, M. and Turjeman, S. and Zurell, D. and Wikelski, M. and Sapir, N. and Eggers, U. and Fiedler, W. and Jeltsch, F. and Nathan, R. Journal of Animal Ecology 10.1111/1365-2656.12898 ft164
es142 p045 c070 s031 Willow warbler Phylloscopus trochilus Landbird M 39 2.40 ICC 0.2690 N NA no Conventional Arrival_breed Milford Common, Surrey Europe -0.650000 51.16600 Breeding Table 2, p. 65 text Individual consistency in the arrival dates of territorial male Willow Warblers Phylloscopus trochilus 2016 Lawn, M. R. Ringing and Migration 10.1080/03078698.2016.1190618 ft071
es143 p045 c071 s031 Willow warbler Phylloscopus trochilus Landbird M 79 2.00 ICC 0.3380 N NA no Conventional Arrival_breed Milford Common, Surrey Europe -0.650000 51.16600 Breeding Table 2, p. 65 text Individual consistency in the arrival dates of territorial male Willow Warblers Phylloscopus trochilus 2016 Lawn, M. R. Ringing and Migration 10.1080/03078698.2016.1190618 ft071
es144 p045 c072 s031 Willow warbler Phylloscopus trochilus Landbird M 16 2.00 ICC 0.6450 N NA no Conventional Arrival_breed Milford Common, Surrey Europe -0.650000 51.16600 Breeding Table 2, p. 65 text Individual consistency in the arrival dates of territorial male Willow Warblers Phylloscopus trochilus 2016 Lawn, M. R. Ringing and Migration 10.1080/03078698.2016.1190618 ft071
es145 p046 c073 s017 Wood thrush Hylocichla mustelina Landbird B 8 2.00 ICC 0.0500 N NA no GLS Depart_breed Pennsylvania North America -79.890000 41.79700 Breeding Table 2, p. 4 text Repeat tracking of individual songbirds reveals consistent migration timing but flexibility in route 2012 Stanley, C. Q. and MacPherson, M. and Fraser, K. C. and McKinnon, E. A. and Stutchbury, B. J. M. PLoS ONE 10.1371/journal.pone.0040688 ft117
es146 p046 c073 s017 Wood thrush Hylocichla mustelina Landbird B 9 2.00 ICC 0.6200 N NA no GLS Nonbreed_arrival Pennsylvania North America -79.890000 41.79700 Breeding Table 2, p. 4 text Repeat tracking of individual songbirds reveals consistent migration timing but flexibility in route 2012 Stanley, C. Q. and MacPherson, M. and Fraser, K. C. and McKinnon, E. A. and Stutchbury, B. J. M. PLoS ONE 10.1371/journal.pone.0040688 ft117
es147 p046 c073 s017 Wood thrush Hylocichla mustelina Landbird B 9 2.00 ICC 0.6600 N NA no GLS Arrival_breed Pennsylvania North America -79.890000 41.79700 Breeding Table 2, p. 4 text Repeat tracking of individual songbirds reveals consistent migration timing but flexibility in route 2012 Stanley, C. Q. and MacPherson, M. and Fraser, K. C. and McKinnon, E. A. and Stutchbury, B. J. M. PLoS ONE 10.1371/journal.pone.0040688 ft117
es148 p046 c073 s017 Wood thrush Hylocichla mustelina Landbird B 10 2.00 ICC 0.7100 N NA no GLS Nonbreed_depart Pennsylvania North America -79.890000 41.79700 Breeding Table 2, p. 4 text Repeat tracking of individual songbirds reveals consistent migration timing but flexibility in route 2012 Stanley, C. Q. and MacPherson, M. and Fraser, K. C. and McKinnon, E. A. and Stutchbury, B. J. M. PLoS ONE 10.1371/journal.pone.0040688 ft117
es149 p047 c074 s036 Pied avocet Recurvirostra avosetta Waterbird M 31 2.94 ICC 0.2900 N NA no Conventional Arrival_breed Wadden Sea Coast, Germany Europe 8.860000 54.63000 Breeding p. 383, text, results text Arrival of Pied Avocets Recurvirostra avosetta at the breeding site: Effects of winter quarters and consequences for reproductive success 2002 Hotker, H. Ardea ft015
es150 p047 c075 s036 Pied avocet Recurvirostra avosetta Waterbird M 28 3.07 ICC 0.0900 N NA no Conventional Arrival_breed Wadden Sea Coast, Germany Europe 8.860000 54.63000 Breeding p. 383, text, results text Arrival of Pied Avocets Recurvirostra avosetta at the breeding site: Effects of winter quarters and consequences for reproductive success 2002 Hotker, H. Ardea ft015
es151 p047 c076 s036 Pied avocet Recurvirostra avosetta Waterbird F 27 2.96 ICC 0.0500 N NA no Conventional Arrival_breed Wadden Sea Coast, Germany Europe 8.860000 54.63000 Breeding p. 383, text, results text Arrival of Pied Avocets Recurvirostra avosetta at the breeding site: Effects of winter quarters and consequences for reproductive success 2002 Hotker, H. Ardea ft015
es152 p047 c077 s036 Pied avocet Recurvirostra avosetta Waterbird F 15 2.33 ICC -0.0800 N NA no Conventional Arrival_breed Wadden Sea Coast, Germany Europe 8.860000 54.63000 Breeding p. 383, text, results text Arrival of Pied Avocets Recurvirostra avosetta at the breeding site: Effects of winter quarters and consequences for reproductive success 2002 Hotker, H. Ardea ft015
es153 p048 c078 s021 Relict gull Larus relictus Seabird B 4 3.50 ICC 0.1300 N NA no Satellite Depart_breed Hongjian Nur, China Not 109.930000 39.14000 Breeding Table 3, p. 7 text Seasonal dispersal and longitudinal migration in the Relict Gull Larus relictus across the Inner-Mongolian Plateau 2017 Liu, D. and Zhang, G. and Jiang, H. and Chen, L. and Meng, D. and Lu, J. PeerJ 10.7717/peerj.3380 ft125
es154 p048 c078 s021 Relict gull Larus relictus Seabird B 4 3.50 ICC 0.8000 N NA no Satellite Nonbreed_arrival Hongjian Nur, China Not 109.930000 39.14000 Breeding Table 3, p. 7 text Seasonal dispersal and longitudinal migration in the Relict Gull Larus relictus across the Inner-Mongolian Plateau 2017 Liu, D. and Zhang, G. and Jiang, H. and Chen, L. and Meng, D. and Lu, J. PeerJ 10.7717/peerj.3380 ft125
es155 p048 c078 s021 Relict gull Larus relictus Seabird B 4 3.00 ICC -0.1800 N NA no Satellite Nonbreed_depart Hongjian Nur, China Not 109.930000 39.14000 Breeding Table 3, p. 7 text Seasonal dispersal and longitudinal migration in the Relict Gull Larus relictus across the Inner-Mongolian Plateau 2017 Liu, D. and Zhang, G. and Jiang, H. and Chen, L. and Meng, D. and Lu, J. PeerJ 10.7717/peerj.3380 ft125
es156 p048 c078 s021 Relict gull Larus relictus Seabird B 4 3.00 ICC -0.0100 N NA no Satellite Arrival_breed Hongjian Nur, China Not 109.930000 39.14000 Breeding Table 3, p. 7 text Seasonal dispersal and longitudinal migration in the Relict Gull Larus relictus across the Inner-Mongolian Plateau 2017 Liu, D. and Zhang, G. and Jiang, H. and Chen, L. and Meng, D. and Lu, J. PeerJ 10.7717/peerj.3380 ft125
es157 p049 c079 s042 Black-browed albatross Thalassarche melanophrys Seabird B 22 2.00 r -0.0700 N NA NA GLS Depart_breed Bird Island, South Georgia Not -38.040000 -54.00600 Breeding p. 2390, text, results text Summer distribution and migration of nonbreeding albatrosses: Individual consistencies and implications for conservation 2005 Phillips, R. A. and Silk, J. R. D. and Croxall, J. P. and Afanasyev, V. and Bennett, V. J. Ecology 10.1890/04-1885 ft133
es158 p049 c079 s042 Black-browed albatross Thalassarche melanophrys Seabird B 22 2.00 r 0.5000 N NA NA GLS Arrival_breed Bird Island, South Georgia Not -38.040000 -54.00600 Breeding p. 2390, text, results text Summer distribution and migration of nonbreeding albatrosses: Individual consistencies and implications for conservation 2005 Phillips, R. A. and Silk, J. R. D. and Croxall, J. P. and Afanasyev, V. and Bennett, V. J. Ecology 10.1890/04-1885 ft133
es159 p049 c079 s042 Black-browed albatross Thalassarche melanophrys Seabird B 21 2.00 r 0.2800 N NA NA GLS Nonbreed_arrival Bird Island, South Georgia Not -38.040000 -54.00600 Breeding p. 2390, text, results text Summer distribution and migration of nonbreeding albatrosses: Individual consistencies and implications for conservation 2005 Phillips, R. A. and Silk, J. R. D. and Croxall, J. P. and Afanasyev, V. and Bennett, V. J. Ecology 10.1890/04-1885 ft133
es160 p049 c079 s042 Black-browed albatross Thalassarche melanophrys Seabird B 21 2.00 r 0.5400 N NA NA GLS Nonbreed_depart Bird Island, South Georgia Not -38.040000 -54.00600 Breeding p. 2390, text, results text Summer distribution and migration of nonbreeding albatrosses: Individual consistencies and implications for conservation 2005 Phillips, R. A. and Silk, J. R. D. and Croxall, J. P. and Afanasyev, V. and Bennett, V. J. Ecology 10.1890/04-1885 ft133
es161 p050 c080 s004 Turnstone Arenaria interpres Waterbird B 39 2.00 r 0.7440 N NA NA Conventional Nonbreed_depart Firth of Clyde Europe -4.770000 55.63000 Non-breeding p. 210, text, Figure 2 legend text Survival, winter population stability and site fidelity in the turnstone aren aria interpres 1985 Metcalfe, N. B. and Furness, R. W. Bird Study 10.1080/00063658509476881 ft134
es162 p050 c081 s004 Turnstone Arenaria interpres Waterbird B 32 2.00 r 0.4600 N NA NA Conventional Nonbreed_depart Firth of Clyde Europe -4.770000 55.63000 Non-breeding p. 210, text, Figure 3 legend text Survival, winter population stability and site fidelity in the turnstone aren aria interpres 1985 Metcalfe, N. B. and Furness, R. W. Bird Study 10.1080/00063658509476881 ft134
es163 p051 c082 s015 Whooping crane Grus americana Waterbird B 23 4.40 ICC 0.4800 Y Age, social status no Satellite Depart_breed Wood Buffalo National Park of Canada (breeding) and wintering sites along the Texas Gulf Coast North America -113.240000 59.30000 both? p. 6, text, results text Heterogeneity in migration strategies of Whooping Cranes 2019 Pearse, A. T. and Metzger, K. L. and Brandt, D. A. and Bidwell, M. T. and Harner, M. J. and Baasch, D. M. and Harrell, W. Condor 10.1093/condor/duz056 ft063
es164 p051 c082 s015 Whooping crane Grus americana Waterbird B 27 4.40 ICC 0.1700 Y Age, social status no Satellite Nonbreed_arrival Wood Buffalo National Park of Canada (breeding) and wintering sites along the Texas Gulf Coast North America -113.240000 59.30000 both? p. 6, text, results text Heterogeneity in migration strategies of Whooping Cranes 2019 Pearse, A. T. and Metzger, K. L. and Brandt, D. A. and Bidwell, M. T. and Harner, M. J. and Baasch, D. M. and Harrell, W. Condor 10.1093/condor/duz056 ft063
es165 p051 c082 s015 Whooping crane Grus americana Waterbird B 30 4.10 ICC 0.4100 Y Age, social status no Satellite Nonbreed_depart Wood Buffalo National Park of Canada (breeding) and wintering sites along the Texas Gulf Coast North America -113.240000 59.30000 both? p. 6, text, results text Heterogeneity in migration strategies of Whooping Cranes 2019 Pearse, A. T. and Metzger, K. L. and Brandt, D. A. and Bidwell, M. T. and Harner, M. J. and Baasch, D. M. and Harrell, W. Condor 10.1093/condor/duz056 ft063
es166 p051 c082 s015 Whooping crane Grus americana Waterbird B 27 4.50 ICC 0.2500 Y Age, social status no Satellite Arrival_breed Wood Buffalo National Park of Canada (breeding) and wintering sites along the Texas Gulf Coast North America -113.240000 59.30000 both? p. 6, text, results text Heterogeneity in migration strategies of Whooping Cranes 2019 Pearse, A. T. and Metzger, K. L. and Brandt, D. A. and Bidwell, M. T. and Harner, M. J. and Baasch, D. M. and Harrell, W. Condor 10.1093/condor/duz056 ft063
es167 p052 c083 s019 Lesser black-backed gull Larus fuscus Seabird B 81 2.85 ICC 0.5100 Y Migration distance (fixed), colony and individual (random) yes Satellite Depart_breed eight colonies in the Netherlands, Belgium and the UK Europe 3.180000 51.30000 Breeding p. 6, text, results text Long-distance migrants vary migratory behaviour as much as short-distance migrants: An individual-level comparison from a seabird species with diverse migration strategies 2021 Brown, J. M. and van Loon, E. E. and Bouten, W. and Camphuysen, K. C. J. and Lens, L. and Muller, W. and Thaxter, C. B. and Shamoun-Baranes, J. Journal of Animal Ecology 10.1111/1365-2656.13431 ft095
es168 p052 c083 s019 Lesser black-backed gull Larus fuscus Seabird B 80 2.86 ICC 0.7700 Y Migration distance (fixed), colony and individual (random) yes Satellite Nonbreed_arrival eight colonies in the Netherlands, Belgium and the UK Europe 3.180000 51.30000 Breeding p. 6, text, results text Long-distance migrants vary migratory behaviour as much as short-distance migrants: An individual-level comparison from a seabird species with diverse migration strategies 2021 Brown, J. M. and van Loon, E. E. and Bouten, W. and Camphuysen, K. C. J. and Lens, L. and Muller, W. and Thaxter, C. B. and Shamoun-Baranes, J. Journal of Animal Ecology 10.1111/1365-2656.13431 ft095
es169 p052 c083 s019 Lesser black-backed gull Larus fuscus Seabird B 82 2.84 ICC 0.5800 Y Migration distance (fixed), colony and individual (random) yes Satellite Nonbreed_depart eight colonies in the Netherlands, Belgium and the UK Europe 3.180000 51.30000 Breeding p. 6, text, results text Long-distance migrants vary migratory behaviour as much as short-distance migrants: An individual-level comparison from a seabird species with diverse migration strategies 2021 Brown, J. M. and van Loon, E. E. and Bouten, W. and Camphuysen, K. C. J. and Lens, L. and Muller, W. and Thaxter, C. B. and Shamoun-Baranes, J. Journal of Animal Ecology 10.1111/1365-2656.13431 ft095
es170 p052 c083 s019 Lesser black-backed gull Larus fuscus Seabird B 82 2.84 ICC 0.5700 Y Migration distance (fixed), colony and individual (random) yes Satellite Arrival_breed eight colonies in the Netherlands, Belgium and the UK Europe 3.180000 51.30000 Breeding p. 6, text, results text Long-distance migrants vary migratory behaviour as much as short-distance migrants: An individual-level comparison from a seabird species with diverse migration strategies 2021 Brown, J. M. and van Loon, E. E. and Bouten, W. and Camphuysen, K. C. J. and Lens, L. and Muller, W. and Thaxter, C. B. and Shamoun-Baranes, J. Journal of Animal Ecology 10.1111/1365-2656.13431 ft095
es171 p053 c084 s040 Common eider Somateria mollissima Waterbird F 24 2.75 ICC 0.5240 N NA yes GLS Nonbreed_depart Kongsfjorden in Svalbard (79<U+00B0>N, 12<U+00B0>E) Europe 11.880000 79.03000 Breeding p. 9, text, results text Repeatability and Flexibility in the Migration Strategies of an Arctic Seabird 2016 Bjorn, N. MSc thesis ft166
es172 p053 c085 s040 Common eider Somateria mollissima Waterbird F 6 2.50 ICC 0.5400 N NA yes GLS Nonbreed_depart Kongsfjorden in Svalbard (79<U+00B0>N, 12<U+00B0>E) Europe 11.880000 79.03000 Breeding p. 9, text, results text Repeatability and Flexibility in the Migration Strategies of an Arctic Seabird 2016 Bjorn, N. MSc thesis ft166
es173 p053 c084 s040 Common eider Somateria mollissima Waterbird F 24 2.75 ICC 0.5390 Y Breeding success yes GLS Nonbreed_arrival Kongsfjorden in Svalbard (79<U+00B0>N, 12<U+00B0>E) Europe 11.880000 79.03000 Breeding p. 9, text, results text Repeatability and Flexibility in the Migration Strategies of an Arctic Seabird 2016 Bjorn, N. MSc thesis ft166
es174 p053 c085 s040 Common eider Somateria mollissima Waterbird F 6 2.50 ICC 0.7440 Y Breeding success yes GLS Nonbreed_arrival Kongsfjorden in Svalbard (79<U+00B0>N, 12<U+00B0>E) Europe 11.880000 79.03000 Breeding p. 9, text, results text Repeatability and Flexibility in the Migration Strategies of an Arctic Seabird 2016 Bjorn, N. MSc thesis ft166
es175 p053 c086 s040 Common eider Somateria mollissima Waterbird F 30 2.70 ICC 0.5350 N NA yes GLS Nonbreed_depart Kongsfjorden in Svalbard (79<U+00B0>N, 12<U+00B0>E) Europe 11.880000 79.03000 Breeding p. 9, text, results text Repeatability and Flexibility in the Migration Strategies of an Arctic Seabird 2016 Bjorn, N. MSc thesis ft166
es176 p053 c086 s040 Common eider Somateria mollissima Waterbird F 30 2.70 ICC 0.6440 Y Breeding success yes GLS Nonbreed_arrival Kongsfjorden in Svalbard (79<U+00B0>N, 12<U+00B0>E) Europe 11.880000 79.03000 Breeding p. 9, text, results text Repeatability and Flexibility in the Migration Strategies of an Arctic Seabird 2016 Bjorn, N. MSc thesis ft166
es177 p054 c087 s047 White-eyed vireo Vireo griseus Landbird M 32 2.00 r 0.6160 N NA NA Conventional Arrival_breed southwestern Virginia North America -81.530000 36.84000 Breeding p. 49, text text Banding Returns, Arrival Pattern, and Site-Fidelity of White-Eyed Vireos 1999 Hopp, S. L. and Kirby, A. and Boone, C. A. The Wilson Bulletin ft017

A. es_ID: Unique ID for each row of data (i.e. each effect size).

B. paper_ID: Unique ID for each paper.

C. cohort_ID: Unique ID for each cohort of birds.

D. species_ID: Unique ID for each species.

E. species_common: Common name of species (taken from paper).

F. species_latin: Latin name of species (taken from paper).

G. taxa: Species split into three ecological groups: ‘waterbird’, ‘seabird’ or ‘landbird’ based on Geen et al. (2019)

H. sex: Male, female or both/unknown.

I. n: Number of individuals.

J. k: Number of observations per individual.

K. est: Method of calculating repeatability: correlation coefficient (r) or intraclass correlation coefficient (ICC).

L. R: Repeatability value.

M. fixed_yn: Any fixed effects (or additional random effects) included in repeatability calculation: yes or no.

N. fixed_var: If yes, what additional variables are included.

O. unstandardized_variance: Whether unstandardized variance components are reported.

P. method: Tracking method: conventional (ringing, colour-ringing), geolocator (geolocation), or GPS (GPS, satellite, PTT).

Q. annual_event: Period of annual cycle which repeatability is measured (arrival to, or departure from, breeding or non-breeding grounds).

R. location: Location of tagging (as written by paper).

S. continent: Continent of tagging location: North America, Europe, or Other.

T. long: Longitude of tagging location.

U. lat: Latitude of tagging location.

V. tag_period: Whether individuals were tagged on the breeding or non-breeding grounds.

W. notes: general comments

X. data_location: Where in the paper the data is located.

Y. data_presentation: Text, figure, or table.

Z. title: Title of the paper.

AA. pub_year: Publication year of paper.

AB: authors: Authors of the paper.

AC: journal: Journal the paper was published in.

AD: DOI: DOI of the paper.

AE: fulltext_ID: An ID that is used to link the paper for data extraction to the record of screened full texts.

Sample sizes

Table S2

Sample sizes for our data set in terms of effect sizes, cohorts, studies, species, and the number of effect sizes in the different levels of categorical variables (factors), split by ecological group (seabird, waterbird and landbird), and overall.

# making a table of sample sizes for different variables
df %>%
    group_by(taxa) %>%
    summarise(`Effect sizes (analyses)` = n(), Cohort = n_distinct(cohort_ID), Studies = n_distinct(authors),
        Species = n_distinct(species_ID), `Arrival at breeding grounds` = sum(annual_event ==
            "Arrival_breed", na.rm = T), `Departure from breeding grounds` = sum(annual_event ==
            "Depart_breed", na.rm = T), `Arrival at non-breeding grounds` = sum(annual_event ==
            "Nonbreed_arrival", na.rm = T), `Departure from non-breeding grounds` = sum(annual_event ==
            "Nonbreed_depart", na.rm = T), `Conventional method` = sum(method ==
            "Conventional", na.rm = T), `GLS method` = sum(method == "GLS", na.rm = T),
        `Satellite method` = sum(method == "Satellite", na.rm = T), Female = sum(sex ==
            "F", na.rm = T), Male = sum(sex == "M", na.rm = T), `Mixed sex` = sum(sex ==
            "B", na.rm = T)) -> n_table1

n_table2 <- t(n_table1[, -1])
colnames(n_table2) <- c("Landbird", "Seabird", "Waterbird")
n_table2 %>%
    as_tibble(rownames = "Number of") %>%
    mutate(All = Landbird + Seabird + Waterbird) %>%
    kable() %>%
    kable_styling("striped", position = "left") %>%
    pack_rows("All data", 1, 4) %>%
    pack_rows("Annual event", 5, 8) %>%
    pack_rows("Tracking method", 9, 11) %>%
    pack_rows("Sex", 12, 14)
Number of Landbird Seabird Waterbird All
All data
Effect sizes (analyses) 54 68 55 177
Cohort 29 29 29 87
Studies 20 14 20 54
Species 18 15 14 47
Annual event
Arrival at breeding grounds 27 29 20 76
Departure from breeding grounds 10 27 7 44
Arrival at non-breeding grounds 8 6 11 25
Departure from non-breeding grounds 9 6 17 32
Tracking method
Conventional method 19 2 21 42
GLS method 19 54 18 91
Satellite method 16 12 16 44
Sex
Female 4 8 10 22
Male 20 8 3 31
Mixed sex 30 52 42 124

Marking locations of studies

Figure 1

worldmap <- map_data("world")  # get map data

# Plot of world with all tracking study locations
world <- ggplot() + geom_polygon(data = worldmap, aes(x = long, y = lat, group = group),
    fill = "grey80", colour = NA, size = 0.1) + coord_fixed(1.3) + theme_minimal() +
    scale_colour_manual(values = c(Seabird = "#009E73", Landbird = "#D55E00", Waterbird = "#0072B2")) +
    geom_point(data = df, aes(x = long, y = lat, colour = taxa, shape = method),
        size = 1.5) + geom_rect(aes(xmin = -12, xmax = 23, ymin = 32, ymax = 64),
    fill = NA, colour = "black", size = 0.3) + theme(panel.grid = element_line(colour = "white"),
    axis.title = element_blank(), axis.text = element_blank(), axis.ticks = element_blank(),
    legend.position = "none")

## Zoomed in plot on Europe
zoom <- ggplot() + geom_polygon(data = worldmap, aes(x = long, y = lat, group = group),
    fill = "grey80", colour = NA, size = 0.2) + coord_sf(xlim = c(-11, 23), ylim = c(32,
    64)) + theme_minimal() + theme(legend.text = element_text(size = 6), legend.title = element_text(size = 7,
    face = "bold"), legend.key.size = unit(1, "mm")) + theme(panel.grid = element_line(colour = "white"),
    axis.title = element_blank(), axis.text = element_blank(), panel.border = element_rect(colour = "black",
        fill = NA, size = 0.5), axis.ticks = element_blank()) + scale_colour_manual(name = "Ecological group",
    values = c(Seabird = "#009E73", Landbird = "#D55E00", Waterbird = "#0072B2")) +
    scale_shape_discrete(name = "Method") + geom_point(data = df, aes(x = long, y = lat,
    shape = method, colour = taxa), size = 1.5)

figure1 <- plot_grid(world, zoom, nrow = 1, rel_widths = c(1.3, 1))
# ggsave('figs/Map_of_tagging_locations.pdf', dpi=600, width=180, height=60,
# units='mm')

figure1

Figure 1. The marking locations of birds for all studies with repeatability estimates collated from the literature and included in analyses, coloured by ecological group (waterbird, seabird, or landbird), and shaped by tracking method (conventional, satellite, or GLS).

Meta-analysis

Calculating effect sizes

We created our effect size (correlation coefficient r and intra-class correlation coefficient ICC and their Fisher’s z transformation Zr) from repeatability values and associated sample sizes. We followed the equations outlined in Holtmann et al. (2017). We converted the negative repeatability estimates (n=13) in our dataset to zero, as they often only reflect noise around a statistical zero (Nakagawa & Schielzeth, 2010), but you can find the meta-analytic and meta-regression models with the negative repeatability estimates included in Tables S12-19.

# Creating new columns for effect sizes (Zr) and sampling variance (VZr)
df$VZr <- df$Zr <- NA

# Calculate effect sizes using custom function
for (i in as.numeric(rownames(df))) {
    r <- df$R[i]
    K <- df$k[i]
    Est <- df$est[i]

    Zr <- Zr_transformation(r, K, Est)

    df$Zr[i] <- Zr

}
# Calculate sampling variances using custom function
for (i in as.numeric(rownames(df))) {
    K <- df$k[i]
    N <- df$n[i]
    Est <- df$est[i]

    VZr <- Calc_SV(K, N, Est)

    df$VZr[i] <- VZr

}

# Add a new column with all negative repeatability values set to zero
df$Zr2 <- ifelse(df$Zr < 0, 0, df$Zr + 0)

Phylogenetic tree

Since multiple bird species (n = 47) are included in this dataset, we needed to consider phylogenetic non-independence in our models (Chamberlain et al., 2012, Cinar et al. 2022). To generate the phylogeny, we used a phylogenetic tree from Jetz et al. (2012) (provided by Benedikt Holtmann), which was prepared on the basis of Hackett backbone (Hackett tree; Hackett et al., 2008). We first searched the tree for the species in our dataset to confirm and correct the species names.

  • Limosa lapponica baueri was assumed to be synonym to Limosa lapponica
  • Limosa limosa limosa and Limosa limosa islandica were assumed to be synonym to Limosa limosa
  • Cygnus columbianus bewickii was assumed to be synonym to Cygnus columbianus
  • Catharacta antarctica lonnbergi was assumed to be synonym to Catharacta antarctica
  • Pterodroma deserta was assumed to be synonym to Pterodroma mollis
  • Chen canagicus was assumed to be synonym to Chen canagica
  • Anser caerulescens atlanticus was assumed to be synonym to Chen caerulescens

We then trimmed the tree to include only the species names in our data set, and computed branch lengths using Grafen’s method (Grafen, 1989) in the compute.brlen function in the R package ape (Paradis & Schliep, 2019).

Figure S3

species_list <- unique(df$species_latin)  # use unique() as some names are repeated
species_list <- gsub(" ", "_", species_list)  # replace spaces with underscore so they match tree

# Load bird supertree based on Hackett's backbone
bird_tree_hackett <- read.tree(here("data", "Hackett.tre"))  # tree provided by Benedikt Holtmann

# Prune phylogentic tree for meta-analysis

# Check the tree bird_tree_hackett # 9993 tips = species str(bird_tree_hackett)
# # has edge (branch) lengths
bird_tree_hackett <- collapse.singles(bird_tree_hackett)

# Get only bird species from the supertree that are also included in collected
# data
bird_tree_species <- as.character(bird_tree_hackett$tip.label)

# Check the overlap of species names between collected data file and the
# supertree All species should be present. If not, species names may not match
# with names in the supertree intersect(bird_tree_species, species_list)
# character(39) - should be 47, need to check which species do/do not match

# gives list of species names which are not matched species_list[!species_list
# %in% bird_tree_species]
#'Limosa_lapponica_baueri'
#'Cygnus_columbianus_bewickii'
#'Limosa_limosa_limosa'
#'Limosa_limosa_islandica'
#'Catharacta_antarctica_lonnbergi'
#'Pterodroma_deserta'
#'Chen_canagicus'
#'Anser_caerulescens_atlanticus'

# making a new list so it is clear that this list of species is an updated
# version compared to the original one
species_list_hackett <- species_list

# Changing subspecies to species, or old genus names from papers to new Only
# Pterodroma deserta: 3 species in the Pterodroma feae/madeira/desertae complex
# were once believed to be subspecies of a single species: Pterodroma mollis
species_list_hackett <- replace(species_list_hackett, species_list_hackett == "Limosa_lapponica_baueri",
    "Limosa_lapponica")
species_list_hackett <- replace(species_list_hackett, species_list_hackett == "Limosa_limosa_limosa" |
    species_list_hackett == "Limosa_limosa_islandica", "Limosa_limosa")
species_list_hackett <- replace(species_list_hackett, species_list_hackett == "Cygnus_columbianus_bewickii",
    "Cygnus_columbianus")
species_list_hackett <- replace(species_list_hackett, species_list_hackett == "Catharacta_antarctica_lonnbergi",
    "Catharacta_antarctica")
species_list_hackett <- replace(species_list_hackett, species_list_hackett == "Pterodroma_deserta",
    "Pterodroma_mollis")
species_list_hackett <- replace(species_list_hackett, species_list_hackett == "Chen_canagicus",
    "Chen_canagica")
species_list_hackett <- replace(species_list_hackett, species_list_hackett == "Anser_caerulescens_atlanticus",
    "Chen_caerulescens")

# Now check and see if all species are present in supertree
# intersect(bird_tree_species, species_list_hackett) # = 47 (not 48, because L.
# l. limosa and L. i. islandica both changed to L. limosa)

# Prune supertree to the list of taxa included in the data
pruned_birds_stree <- drop.tip(bird_tree_hackett, bird_tree_hackett$tip.label[-match(species_list_hackett,
    bird_tree_hackett$tip.label)])

# Check if tree is binary and ultrametric is.binary(pruned_birds_stree) # TRUE
# is.ultrametric(pruned_birds_stree) # TRUE

# Save pruned tree to be use in the meta-analysis
write.tree(pruned_birds_stree, file = here("data", "birds_meta-analysis_tree.tre"),
    append = FALSE, digits = 10, tree.names = FALSE)

# Make phylogenetic correlation matrix

# Using metafor - correlation matrix for species in tree
varcor <- vcv(pruned_birds_stree, corr = TRUE)

# Create a column called phylogeny, which matches the tree Copied column to
# edit species names to match tree (as some sp. names are from old papers, or
# are subspecies)
df$species_latin_hackett <- as.character(df$species_latin)
df$species_latin_hackett <- replace(df$species_latin_hackett, df$species_latin_hackett ==
    "Limosa lapponica baueri", "Limosa lapponica")
df$species_latin_hackett <- replace(df$species_latin_hackett, df$species_latin_hackett ==
    "Limosa limosa limosa" | df$species_latin_hackett == "Limosa limosa islandica",
    "Limosa limosa")
df$species_latin_hackett <- replace(df$species_latin_hackett, df$species_latin_hackett ==
    "Cygnus columbianus bewickii", "Cygnus columbianus")
df$species_latin_hackett <- replace(df$species_latin_hackett, df$species_latin_hackett ==
    "Catharacta antarctica lonnbergi", "Catharacta antarctica")
df$species_latin_hackett <- replace(df$species_latin_hackett, df$species_latin_hackett ==
    "Pterodroma deserta", "Pterodroma mollis")
df$species_latin_hackett <- replace(df$species_latin_hackett, df$species_latin_hackett ==
    "Chen canagicus", "Chen canagica")
df$species_latin_hackett <- replace(df$species_latin_hackett, df$species_latin_hackett ==
    "Anser caerulescens atlanticus", "Chen caerulescens")

df$phylogeny <- gsub(" ", "_", df$species_latin_hackett)

# Plot tree to see how it looks like
plot(pruned_birds_stree, label.offset = 2, cex = 0.8, main = "'Hackett tree'", cex.main = 1,
    line = 0.5)  # with branch lengths

Figure S3. Phylogenetic tree (with Hackett backbone) used for phylogenetic meta-analysis and meta-regression on repeatability in avian migratory timings.

Meta-analytic model

Running multilevel meta-analytic model with and without phylogeny

We used the rma.mv function from the package metafor (Viechtbauer, 2010) to run all meta-analytic models and meta-regressions. This function allows us to incorporate variance-covariance matrices in the V term. We assumed a correlation of 0.5 (Noble et al., 2017) that will be fitted as part of the random effect structure of our models but also test different levels of correlation (see Sensitivity analysis).

As we have a multi-species dataset, we use the model that accounts for both the non-phylogenetic and phylogenetic species-level variance in addition to the full multilevel structure of the data as any attempts to simplify this model, such as using only the phylogenetic variance component, may lead to erroneous inferences from the data (Cinar et al. 2022).

# Create a variance-covariance matrix at the cohort level
VCV <- impute_covariance_matrix(vi = df$VZr, cluster = df$cohort_ID, r = 0.5)
# Why 0.5 - see https://onlinelibrary.wiley.com/doi/full/10.1111/mec.14031

ma_model1 <- rma.mv(yi = Zr2, V = VCV,
                   random = list(~1 | es_ID, 
                                 ~1 | paper_ID, 
                                 ~1 | cohort_ID, 
                                 ~1 | species_ID), 
                   data = df)

ma_model2 <- rma.mv(yi = Zr2, V = VCV, 
                      random = list(~1 | es_ID, 
                                    ~1 | paper_ID, 
                                    ~1 | cohort_ID, 
                                    ~1 | species_ID, # non-phylo effect
                                    ~1 | phylogeny), # phylo effect
                      R = list(phylogeny = varcor), # phylogenetic relatedness
                      data = df)

Table S3

Overall effects (meta-analytic means) and 95% confidence intervals (CIs) both in Zr and back-transformed to ICC, and heterogeneity, I2, for the multilevel intercept-only meta-analysis models including and excluding phylogeny.

## # estimating I2 as measure of heterogeneity
i2_ma1 <- round(i2_ml(ma_model1) * 100, 1)
i2_ma2 <- round(i2_ml(ma_model2) * 100, 1)

# Back-transform to ICC
ma1 <- mod_results(ma_model1, mod = "Int")
ma1_mod_table <- ma1$mod_table

ma2 <- mod_results(ma_model2, mod = "Int")
ma2_mod_table <- ma2$mod_table

# need to calculate k for whole data set to use in formula

k_all <- mean(df$k)

for (i in names(ma1_mod_table)[2:6]) {

    ma1_mod_table[i] <- Zr_to_ICC(ma1_mod_table[i], k_all)

}

for (i in names(ma2_mod_table)[2:6]) {

    ma2_mod_table[i] <- Zr_to_ICC(ma2_mod_table[i], k_all)

}

# creating a table
tibble(Model = c("Meta-analysis (Zr)", "Meta-analysis (ICC)", "Meta-analysis phylo (Zr)",
    "Meta-analysis phylo (ICC)"), `Overall mean` = c(ma_model1$b, ma1_mod_table$estimate,
    ma_model2$b, ma2_mod_table$estimate), `Lower CI [0.025]` = c(ma_model1$ci.lb,
    ma1_mod_table$lowerCL, ma_model2$ci.lb, ma2_mod_table$lowerCL), `Upper CI [0.975]` = c(ma_model1$ci.ub,
    ma1_mod_table$upperCL, ma_model2$ci.ub, ma2_mod_table$upperCL), `I^2^~total~` = c(i2_ma1[1],
    NA, i2_ma2[1], NA), `I^2^~es~` = c(i2_ma1[2], NA, i2_ma2[2], NA), `I^2^~paper~` = c(i2_ma1[3],
    NA, i2_ma2[3], NA), `I^2^~cohort~` = c(i2_ma1[4], NA, i2_ma2[4], NA), `I^2^~species~` = c(i2_ma1[5],
    NA, i2_ma2[5], NA), `I^2^~phylo~` = c(NA, NA, i2_ma2[6], NA), ) %>%
    kable("html", digits = 3) %>%
    kable_styling("striped", position = "left")
Model Overall mean Lower CI [0.025] Upper CI [0.975] I2total I2es I2paper I2cohort I2species I2phylo
Meta-analysis (Zr) 0.541 0.445 0.637 84.0 50.6 0 0 33.4 NA
Meta-analysis (ICC) 0.421 0.348 0.490 NA NA NA NA NA NA
Meta-analysis phylo (Zr) 0.532 0.400 0.664 84.2 49.7 0 0 27.3 7.2
Meta-analysis phylo (ICC) 0.414 0.313 0.508 NA NA NA NA NA NA

Figure 2a

ma2_data <- ma2$data

# back-transform model results to ICC
# need to calculate k for whole data set to use in formula

ma2_data$yi_ICC <- Zr_to_ICC(ma2_data$yi, k_all)

ma2_data$moderator <- factor(ma2_data$moderator, levels = ma2_mod_table$name, labels = ma2_mod_table$name)
ma2_data$scale <- (1/sqrt(ma2_data[,"vi"]))
ma2_mod_table$K <- as.vector(by(ma2_data, ma2_data[,"moderator"], function(x) length(x[,"yi"])))
ma2_group_no <- nrow(ma2_mod_table)

# colour blind friendly colours with grey
cbpl <- c("#E69F00","#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#56B4E9", "#999999")

# creating an orchard plot
fig_ma2 <- ggplot(data = ma2_mod_table, aes(x = estimate, y = "Overall mean")) + 
     scale_x_continuous(limits = c(-0.37, 1), breaks = seq(-0.4, 1, by = 0.2), labels = c("-0.4", "-0.2", "0.0", "0.2", "0.4", "0.6", "0.8", "1.0")) +
    ggbeeswarm::geom_quasirandom(data = ma2_data, aes(x = yi_ICC, y = "Overall mean", size = scale), fill="black", groupOnX=FALSE, alpha = 0.2) +
    geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = F, size = 0.5, alpha = 0.6) +
    geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend = F, size = 1.2) + 
    geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
    geom_point(fill="black", size = 3, shape = 21) + 
    annotate('text', x = 0.93, y = (seq(1, ma2_group_no, 1)+0.35), label= paste("italic(k)==", ma2_mod_table$K), parse=TRUE, hjust = "left", size=3.5) +
    ggplot2::theme_bw() +
    ggplot2::guides(fill="none", colour="none") +
    ggplot2::theme(legend.position = c(1,0), legend.justification = c(1,0), legend.title = element_text(size=8), 
                   legend.text = element_text(size=8), legend.key = element_rect(colour = NA, fill = NA), 
                   legend.background = element_blank(), legend.direction = "horizontal",
                   axis.text.x = element_text(size=9),
                   axis.text.y = element_text(size=9, colour="black", hjust=0.5, angle=90), 
                   axis.title.y = element_blank(), legend.key.size = unit(0.5,'cm')) +
    ggplot2::labs(size=paste("Precision (1/SE)"), x=paste("Effect size (ICC)"))

# For Figure 2

a <- ggplot(data = ma2_mod_table, aes(x = estimate, y = "Overall mean")) + 
     scale_x_continuous(limits = c(-0.37, 1), breaks = seq(-0.4, 1, by = 0.2), labels = c("-0.4", "-0.2", "0.0", "0.2", "0.4", "0.6", "0.8", "1.0")) +
    ggbeeswarm::geom_quasirandom(data = ma2_data, aes(x = yi_ICC, y = "Overall mean", size = scale), fill="black", groupOnX=FALSE, alpha = 0.2) +
    geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = F, size = 0.5, alpha = 0.6) + # CI
    geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend = F, size = 1.2) + 
    geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
    geom_point(fill="black", size = 3, shape = 21) + 
    annotate('text', x = 0.93, y = (seq(1, ma2_group_no, 1)+0.35), label= paste("italic(k)==", ma2_mod_table$K), parse=TRUE, hjust = "left", size=3.5) +
    ggplot2::theme_bw() +
    ggplot2::guides(fill="none", colour="none") +
    ggplot2::theme(legend.position = c(1,0), legend.justification = c(1,0), legend.title = element_text(size=8), 
                   legend.text = element_text(size=8), legend.key = element_rect(colour = NA, fill = NA), 
                   legend.background = element_blank(), legend.direction = "horizontal", 
                   axis.text.y = element_text(size=9, colour="black", hjust=0.5, angle=90), 
                   axis.title = element_blank(), legend.key.size = unit(0.5,'cm')) +
    ggplot2::labs(size=paste("Precision (1/SE)"))

fig_ma2

Figure 2a. Repeatability of avian migration timing for all estimates together. The plot shows the phylogenetic meta-analytic mean with 95% confidence intervals (thick lines, indicating uncertainty around the overall estimate) and 95% prediction intervals (thin lines, indicating the possible range for 95% of new (or simply not included) effect sizes), observed effect sizes (back-transformed to ICC) scaled by precision (circles) and k = number of effect sizes. The graph was constructed using code adapted from the orchard_plot function in the orchaRd package (Nakagawa, Lagisz, O’Dea, et al., 2021).

Meta-regression

We ran a univariate meta-regression model for each of the following moderators: 1) annual_event, 2) method, 3) taxa, 4) sex and 5) number of samples per individual (k).

Univariate (uni-predictor) analyses

We first conducted a series of meta-regression models with each of the moderators introduced above.

Period of the annual cycle

# meta-regression: mutiple intercepts
meta_regression1 <- rma.mv(yi = Zr2, V = VCV, 
                           mods = ~ annual_event,
                           random = list(~1 | es_ID, 
                                         ~1 | paper_ID, 
                                         ~1 | cohort_ID, 
                                         ~1 | species_ID, 
                                         ~1 | phylogeny),
                           R = list(phylogeny = varcor), # added in phylogney
                           data = df)

meta_regression1b <- rma.mv(yi = Zr2, V = VCV, 
                            mods = ~ relevel(annual_event, ref = "Depart_breed"),
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor), # added in phylogney
                            data = df)


meta_regression1c <- rma.mv(yi = Zr2, V = VCV, 
                            mods = ~ relevel(annual_event, ref = "Nonbreed_depart"),
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor), # added in phylogney
                            data = df)

# meta-regression: contrast (for orchard plot)
meta_regression1d <- rma.mv(yi = Zr2, V = VCV, 
                            mods = ~ annual_event -1,
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor), # added in phylogney
                            data = df)

Table S4

Regression coefficients (Estimate) and 95% confidence intervals (CIs) both in Zr and back-transformed to ICC from the meta-regression with annual_event. Note that mu means the group mean while beta represents the contrast between two groups in the Unit column. R2marginal = 10.5%.

# getting marginal R2
r2_meta_regression1 <- r2_ml(meta_regression1)

# getting estimates including back-transformation to ICC

mr1 <- mod_results(meta_regression1d, mod = "annual_event")
mr1_mod_table <- mr1$mod_table

mr1_data <- mr1$data

# calculate k for each method separately

# df %>% group_by(annual_event) %>% summarise(mean(k))

mr1_data <- mr1_data %>%
    mutate(k = case_when(moderator == "Arrival_breed" ~ 2.55, moderator == "Depart_breed" ~
        2.45, moderator == "Nonbreed_arrival" ~ 3.2, moderator == "Nonbreed_depart" ~
        2.94))

mr1_data$yi_ICC <- Zr_to_ICC(mr1_data$yi, mr1_data$k)

mr1_mod_table$k <- c(2.55, 2.45, 3.2, 2.94)

for (i in names(mr1_mod_table)[2:6]) {

    mr1_mod_table[i] <- Zr_to_ICC(mr1_mod_table[i], mr1_mod_table$k)

}

# creating a table
tibble(`Fixed effect` = c(rep(as.character(mr1_mod_table$name), 2), cont_gen(mr1_mod_table$name)),
    Unit = c(rep(c("Zr (mu)", "ICC (mu)"), each = 4), rep("Zr (beta)", 6)), Estimate = c(meta_regression1d$b,
        mr1_mod_table$estimate, meta_regression1$b[-1], meta_regression1b$b[-(1:2)],
        meta_regression1c$b[-(1:3)]), `Lower CI [0.025]` = c(meta_regression1d$ci.lb,
        mr1_mod_table$lowerCL, meta_regression1$ci.lb[-1], meta_regression1b$ci.lb[-(1:2)],
        meta_regression1c$ci.lb[-(1:3)]), `Upper CI  [0.975]` = c(meta_regression1d$ci.ub,
        mr1_mod_table$upperCL, meta_regression1$ci.ub[-1], meta_regression1b$ci.ub[-(1:2)],
        meta_regression1c$ci.ub[-(1:3)])) -> t_annual_event
# `R2` = c(r2_meta_regression1[1], rep(NA,13))) ->

t_annual_event %>%
    kable("html", digits = 3) %>%
    kable_styling("striped", position = "left")
Fixed effect Unit Estimate Lower CI [0.025] Upper CI [0.975]
Arrival_breed Zr (mu) 0.472 0.308 0.637
Depart_breed Zr (mu) 0.391 0.206 0.576
Nonbreed_arrival Zr (mu) 0.593 0.396 0.791
Nonbreed_depart Zr (mu) 0.719 0.531 0.908
Arrival_breed ICC (mu) 0.381 0.250 0.503
Depart_breed ICC (mu) 0.326 0.172 0.469
Nonbreed_arrival ICC (mu) 0.416 0.274 0.547
Nonbreed_depart ICC (mu) 0.522 0.391 0.636
Arrival_breed-Depart_breed Zr (beta) -0.081 -0.225 0.062
Arrival_breed-Nonbreed_arrival Zr (beta) 0.121 -0.048 0.290
Arrival_breed-Nonbreed_depart Zr (beta) 0.247 0.088 0.405
Depart_breed-Nonbreed_arrival Zr (beta) 0.202 0.023 0.382
Depart_breed-Nonbreed_depart Zr (beta) 0.328 0.157 0.500
Nonbreed_arrival-Nonbreed_depart Zr (beta) -0.126 -0.301 0.049

Figure 2b

# Orchard plot for annual event with ICC

mr1_data$moderator <- factor(mr1_data$moderator, levels = mr1_mod_table$name, labels = mr1_mod_table$name)
mr1_data$scale <- (1/sqrt(mr1_data[, "vi"]))
mr1_mod_table$K <- as.vector(by(mr1_data, mr1_data[, "moderator"], function(x) length(x[,
    "yi"])))
mr1_group_no <- nrow(mr1_mod_table)

image_ABegg <- readPNG(here("images/AB_eggs_icon.png"))
image_ANB <- readPNG(here("images/ANB_icon.png"))
image_DBchick <- readPNG(here("images/DB_chicks_icon.png"))
image_DNB <- readPNG(here("images/DNB_icon.png"))

# creating an orchard plot

# fig_annual_event <- ggplot(data = mr1_mod_table, aes(x = estimate, y = name))
# + scale_x_continuous(limits = c(-0.4, 1), breaks = seq(-0.4, 1, by = 0.2),
# labels = c('-0.4', '-0.2', '0.0', '0.2', # '0.4', '0.6', '0.8', '1.0')) +
# ggbeeswarm::geom_quasirandom(data = mr1_data, aes(x = yi_ICC, y = moderator,
# size = scale, colour=moderator), groupOnX=FALSE, # alpha = 0.4) +
# geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend =
# F, size = 0.5, alpha = 0.6) + # CI geom_errorbarh(aes(xmin = lowerCL, xmax =
# upperCL), height = 0, show.legend = F, size = 1.2) + geom_vline(xintercept =
# 0, linetype = 2, colour = 'black', alpha = 0.5) + geom_point(aes(fill=name),
# size = 3, shape = 21) + annotate('text', x = 0.93, y = (seq(1, mr1_group_no,
# 1)+0.35), label= paste('italic(k)==', mr1_mod_table$K), parse=TRUE, hjust # =
# 'left', size=3.5) + ggplot2::theme_bw() + ggplot2::guides(fill='none',
# colour='none') + ggplot2::theme(axis.text.y = element_text(size=9,
# colour='black', hjust=0.5, angle=90), legend.position = 'none', axis.title.y
# = element_blank(), axis.text.x = element_text(size=9)) +
# scale_y_discrete(labels=c('Arrival_breed' = 'Arrive', 'Depart_breed' =
# 'Depart', 'Nonbreed_arrival' = 'Arrive', # 'Nonbreed_depart' = 'Depart')) +
# \t scale_fill_manual(values=cbpl) + \t scale_colour_manual(values=cbpl) +
# ggplot2::labs(size=paste('Precision (1/SE)'), x=paste('Effect size (ICC)')) +
# annotation_custom(grid::rasterGrob(image_DNB), xmin = -0.45, xmax = -0.25,
# ymin = 3.7, ymax = 4.3) + annotation_custom(grid::rasterGrob(image_ANB), xmin
# = -0.45, xmax = -0.25, ymin = 2.7, ymax = 3.3) +
# annotation_custom(grid::rasterGrob(image_DBchick), xmin = -0.45, xmax =
# -0.25, ymin = 1.7, ymax = 2.3) +
# annotation_custom(grid::rasterGrob(image_ABegg), xmin = -0.45, xmax = -0.25,
# ymin = 0.7, ymax = 1.3) # for Figure 2 b <- ggplot(data = mr1_mod_table,
# aes(x = estimate, y = name)) + scale_x_continuous(limits = c(-0.37, 1),
# breaks = seq(-0.4, 1, by = 0.2), labels = c('-0.4', '-0.2', '0.0', '0.2',
# '0.4', '0.6', '0.8', '1.0')) + ggbeeswarm::geom_quasirandom(data = mr1_data,
# aes(x = yi_ICC, y = moderator, size = scale, colour=moderator),
# groupOnX=FALSE, alpha = 0.4) + geom_errorbarh(aes(xmin = lowerPR, xmax =
# upperPR), height = 0, show.legend = F, size = 0.5, alpha = 0.6) + # CI
# geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend =
# F, size = 1.2) + geom_vline(xintercept = 0, linetype = 2, colour = 'black',
# alpha = 0.5) + geom_point(aes(fill=name), size = 3, shape = 21) +
# annotate('text', x = 0.93, y = (seq(1, mr1_group_no, 1)+0.35), label=
# paste('italic(k)==', mr1_mod_table$K), parse=TRUE, hjust = 'left', size=3.5)
# + ggplot2::theme_bw() + ggplot2::guides(fill='none', colour='none') +
# ggplot2::theme(axis.text.y = element_text(size=9, colour='black', hjust=0.5,
# angle=90), legend.position = 'none', axis.title = element_blank()) +
# scale_y_discrete(labels=c('Arrival_breed' = 'Arrive', 'Depart_breed' =
# 'Depart', 'Nonbreed_arrival' = 'Arrive', 'Nonbreed_depart' = 'Depart')) + \t
# scale_fill_manual(values=cbpl) + \t scale_colour_manual(values=cbpl) +
# annotation_custom(grid::rasterGrob(image_DNB), xmin = -0.45, xmax = -0.25,
# ymin = 3.7, ymax = 4.3) + annotation_custom(grid::rasterGrob(image_ANB), xmin
# = -0.45, xmax = -0.25, ymin = 2.7, ymax = 3.3) +
# annotation_custom(grid::rasterGrob(image_DBchick), xmin = -0.45, xmax =
# -0.25, ymin = 1.7, ymax = 2.3) +
# annotation_custom(grid::rasterGrob(image_ABegg), xmin = -0.45, xmax = -0.25,
# ymin = 0.7, ymax = 1.3)

# fig_annual_event

Figure 2b. Repeatability of avian migration timing for annual migration events. The plot shows the group-wise means with 95% confidence intervals (thick lines) and 95% prediction intervals (thin lines), observed effect sizes (back-transformed to ICC) scaled by precision (circles) and k = number of effect sizes. Note this plot was edited in Affinity Designer after construction in R.

The effect of tracking method

meta_regression2 <- rma.mv(yi = Zr2, V = VCV, mods = ~method, random = list(~1 |
    es_ID, ~1 | paper_ID, ~1 | cohort_ID, ~1 | species_ID, ~1 | phylogeny), R = list(phylogeny = varcor),
    data = df)

# reordering df$method <- factor(df$method, levels = c('Conventional', 'GLS',
# 'Satellite'))

meta_regression2b <- rma.mv(yi = Zr2, V = VCV, mods = ~relevel(method, ref = "GLS"),
    random = list(~1 | es_ID, ~1 | paper_ID, ~1 | cohort_ID, ~1 | species_ID, ~1 |
        phylogeny), R = list(phylogeny = varcor), data = df)

# Orchard plot - need meta-regression without intercept
meta_regression2c <- rma.mv(yi = Zr2, V = VCV, mods = ~method - 1, random = list(~1 |
    es_ID, ~1 | paper_ID, ~1 | cohort_ID, ~1 | species_ID, ~1 | phylogeny), R = list(phylogeny = varcor),
    data = df)

Table S5

Regression coefficients (Estimate) and 95% confidence intervals (CIs) both in Zr and back-transformed to ICC from the meta-regression with method. Note that mu means the group mean while beta represents the contrast between two groups in the Unit column. R2marginal = 5.1%.

# getting marginal R2
r2_meta_regression2 <- r2_ml(meta_regression2)

# getting estimates including back-transformation to ICC

mr2 <- mod_results(meta_regression2c, mod = "method")
mr2_mod_table <- mr2$mod_table

mr2_data <- mr2$data

# calculate k for each method separately

# df %>% group_by(method) %>% summarise(mean(k))


mr2_data <- mr2_data %>%
    mutate(k = case_when(moderator == "GLS" ~ 2.2, moderator == "Conventional" ~
        3.13, moderator == "Satellite" ~ 3.28))

mr2_data$yi_ICC <- Zr_to_ICC(mr2_data$yi, mr2_data$k)

mr2_mod_table$k <- c(3.13, 2.2, 3.28)

for (i in names(mr2_mod_table)[2:6]) {

    mr2_mod_table[i] <- Zr_to_ICC(mr2_mod_table[i], mr2_mod_table$k)

}

# creating a table
tibble(`Fixed effect` = c(rep(as.character(mr2_mod_table$name), 2), cont_gen(mr2_mod_table$name)),
    Unit = c(rep(c("Zr (mu)", "ICC (mu)"), each = 3), rep("Zr (beta)", 3)), Estimate = c(meta_regression2c$b,
        mr2_mod_table$estimate, meta_regression2$b[-1], meta_regression2b$b[-(1:2)]),
    `Lower CI [0.025]` = c(meta_regression2c$ci.lb, mr2_mod_table$lowerCL, meta_regression2$ci.lb[-1],
        meta_regression2b$ci.lb[-(1:2)]), `Upper CI  [0.975]` = c(meta_regression2c$ci.ub,
        mr2_mod_table$upperCL, meta_regression2$ci.ub[-1], meta_regression2b$ci.ub[-(1:2)])) ->
    t_method
# `R2` = c(r2_meta_regression2[1], rep(NA,8))) -> t_method

t_method %>%
    kable("html", digits = 3) %>%
    kable_styling("striped", position = "left")
Fixed effect Unit Estimate Lower CI [0.025] Upper CI [0.975]
Conventional Zr (mu) 0.434 0.291 0.576
GLS Zr (mu) 0.599 0.456 0.742
Satellite Zr (mu) 0.638 0.428 0.847
Conventional ICC (mu) 0.306 0.202 0.409
GLS ICC (mu) 0.512 0.404 0.608
Satellite ICC (mu) 0.440 0.292 0.575
Conventional-GLS Zr (beta) 0.165 -0.026 0.356
Conventional-Satellite Zr (beta) 0.204 -0.049 0.457
GLS-Satellite Zr (beta) 0.039 -0.215 0.292

Figure 2c

mr2_data$moderator <- factor(mr2_data$moderator, levels = mr2_mod_table$name, labels = mr2_mod_table$name)
mr2_data$scale <- (1/sqrt(mr2_data[,"vi"]))
mr2_mod_table$K <- as.vector(by(mr2_data, mr2_data[,"moderator"], function(x) length(x[,"yi"])))
mr2_group_no <- nrow(mr2_mod_table)

image_conventional <- readPNG(here("images/Conventional_icon.png"))
image_GLS <- readPNG(here("images/GLS_icon.png"))
image_satellite <- readPNG(here("images/Satellite_icon.png"))

# creating orchaRd plot
fig_method <- ggplot(data = mr2_mod_table, aes(x = estimate, y = name)) + 
    scale_x_continuous(limits = c(-0.37, 1), breaks = seq(-0.4, 1, by = 0.2), labels = c("-0.4", "-0.2", "0.0", "0.2", "0.4", "0.6", "0.8", "1.0")) + 
    ggbeeswarm::geom_quasirandom(data = mr2_data, aes(x = yi_ICC, y = moderator, size = scale, colour=moderator), groupOnX=FALSE, alpha = 0.4) +
    geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = F, size = 0.5, alpha = 0.6) + # CI
    geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend = F, size = 1.2) + 
    geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
    geom_point(aes(fill=name), size = 3, shape = 21) + 
    annotate('text', x = 0.93, y = (seq(1, mr2_group_no, 1)+0.35), label= paste("italic(k)==", mr2_mod_table$K), parse=TRUE, hjust = "left", size=3.5) +
    scale_color_manual(values = c("Conventional" = "#CC79A7",  "GLS" = "#D55E00",  "Satellite"= "#0072B2")) +
  scale_fill_manual(values = c("Conventional" = "#CC79A7",  "GLS" = "#D55E00",  "Satellite"= "#0072B2")) +
    ggplot2::theme_bw() +
    ggplot2::guides(fill="none", colour="none") +
    ggplot2::theme(axis.text.y = element_text(size=9, colour="black", hjust=0.5, angle=90),
                   legend.position = "none", axis.title.y = element_blank(), axis.text.x=element_text(size=9)) +
    annotation_custom(grid::rasterGrob(image_satellite), xmin = -0.45, xmax = -0.25, ymin = 2.7, ymax = 3.3) +
    annotation_custom(grid::rasterGrob(image_GLS), xmin = -0.45, xmax = -0.25, ymin = 1.7, ymax = 2.3) +
    annotation_custom(grid::rasterGrob(image_conventional), xmin = -0.45, xmax = -0.25, ymin = 0.7, ymax = 1.3) +
  labs(x="Effect size (ICC)")

# for Figure 2

#c <- ggplot(data = mr2_mod_table, aes(x = estimate, y = name)) + 
#    scale_x_continuous(limits = c(-0.37, 1), breaks = seq(-0.4, 1, by = 0.2), labels = c("-0.4", "-0.2", "0.0", "0.2", "0.4", "0.6", "0.8", "1.0")) + 
#    ggbeeswarm::geom_quasirandom(data = mr2_data, aes(x = yi_ICC, y = moderator, size = scale, colour=moderator), groupOnX=FALSE, alpha = 0.4) +
#    geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = F, size = 0.5, alpha = 0.6) + # CI
#    geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend = F, size = 1.2) + 
#    geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
#    geom_point(aes(fill=name), size = 3, shape = 21) + 
#    annotate('text', x = 0.93, y = (seq(1, mr2_group_no, 1)+0.35), label= paste("italic(k)==", mr2_mod_table$K), parse=TRUE, hjust = "left", size=3.5) +
#    scale_color_manual(values = c("Conventional" = "#CC79A7",  "GLS" = "#D55E00",  "Satellite"= "#0072B2")) +
#  scale_fill_manual(values = c("Conventional" = "#CC79A7",  "GLS" = "#D55E00",  "Satellite"= "#0072B2")) +
#    ggplot2::theme_bw() +
#    ggplot2::guides(fill="none", colour="none") +
#    ggplot2::theme(axis.text.y = element_text(size=9, colour="black", hjust=0.5, angle=90),
#                   legend.position = "none", axis.title = element_blank()) +
#    annotation_custom(grid::rasterGrob(image_satellite), xmin = -0.45, xmax = -0.25, ymin = 2.7, ymax = 3.3) +
#    annotation_custom(grid::rasterGrob(image_GLS), xmin = -0.45, xmax = -0.25, ymin = 1.7, ymax = 2.3) +
#    annotation_custom(grid::rasterGrob(image_conventional), xmin = -0.45, xmax = -0.25, ymin = 0.7, ymax = 1.3)

fig_method

Figure 2c. Repeatability of avian migration timing for tracking method. The plot shows the group-wise means with 95% confidence intervals (thick lines) and 95% prediction intervals (thin lines), observed effect sizes (back-transformed to ICC) scaled by precision (circles) and k = number of effect sizes.

The effect of sex

# reordering df$sex <- factor(df$sex, levels = c('F', 'M', 'B'))

meta_regression4 <- rma.mv(yi = Zr2, V = VCV, mods = ~sex, random = list(~1 | es_ID,
    ~1 | paper_ID, ~1 | cohort_ID, ~1 | species_ID, ~1 | phylogeny), R = list(phylogeny = varcor),
    data = df)

meta_regression4b <- rma.mv(yi = Zr2, V = VCV, mods = ~relevel(sex, ref = "M"), random = list(~1 |
    es_ID, ~1 | paper_ID, ~1 | cohort_ID, ~1 | species_ID, ~1 | phylogeny), R = list(phylogeny = varcor),
    data = df)

# Orchard plot - need meta-regression without intercept
meta_regression4c <- rma.mv(yi = Zr2, V = VCV, mods = ~sex - 1, random = list(~1 |
    es_ID, ~1 | paper_ID, ~1 | cohort_ID, ~1 | species_ID, ~1 | phylogeny), R = list(phylogeny = varcor),
    data = df)

Table S6

Regression coefficients (Estimate) and 95% confidence intervals (CIs) both in Zr and back-transformed to ICC from the meta-regression with sex. Note that mu means the group mean while beta represents the contrast between two groups in the Unit column. R2marginal = 6.9%. B = both sexes, M = male, and F = female.

# getting marginal R2
r2_meta_regression4 <- r2_ml(meta_regression4)

# getting estimates including back-transformation to ICC

mr4 <- mod_results(meta_regression4c, mod = "sex")
mr4_mod_table <- mr4$mod_table

mr4_data <- mr4$data

# calculate k for each method separately

# df %>% group_by(sex) %>% summarise(mean(k))


mr4_data <- mr4_data %>%
    mutate(k = case_when(moderator == "F" ~ 2.37, moderator == "M" ~ 2.38, moderator ==
        "B" ~ 2.82))

mr4_data$yi_ICC <- Zr_to_ICC(mr4_data$yi, mr4_data$k)

mr4_mod_table$k <- c(2.37, 2.38, 2.82)

for (i in names(mr4_mod_table)[2:6]) {

    mr4_mod_table[i] <- Zr_to_ICC(mr4_mod_table[i], mr4_mod_table$k)

}

# creating a table
tibble(`Fixed effect` = c(rep(as.character(mr4_mod_table$name), 2), cont_gen(mr4_mod_table$name)),
    Unit = c(rep(c("Zr (mu)", "ICC (mu)"), each = 3), rep("Zr (beta)", 3)), Estimate = c(meta_regression4c$b,
        mr4_mod_table$estimate, meta_regression4$b[-1], meta_regression4b$b[-(1:2)]),
    `Lower CI [0.025]` = c(meta_regression4c$ci.lb, mr4_mod_table$lowerCL, meta_regression4$ci.lb[-1],
        meta_regression4b$ci.lb[-(1:2)]), `Upper CI  [0.975]` = c(meta_regression4c$ci.ub,
        mr4_mod_table$upperCL, meta_regression4$ci.ub[-1], meta_regression4b$ci.ub[-(1:2)])) ->
    t_sex
# `R2` = c(r2_meta_regression4[1], rep(NA,8))) -> t_sex

t_sex %>%
    kable("html", digits = 3) %>%
    kable_styling("striped", position = "left")
Fixed effect Unit Estimate Lower CI [0.025] Upper CI [0.975]
B Zr (mu) 0.606 0.496 0.715
F Zr (mu) 0.471 0.267 0.675
M Zr (mu) 0.380 0.205 0.554
B ICC (mu) 0.499 0.417 0.573
F ICC (mu) 0.397 0.229 0.545
M ICC (mu) 0.287 0.152 0.419
B-F Zr (beta) -0.135 -0.359 0.089
B-M Zr (beta) -0.226 -0.425 -0.027
F-M Zr (beta) 0.091 -0.144 0.327

Figure 2e

mr4_data$moderator <- factor(mr4_data$moderator, levels = mr4_mod_table$name, labels = mr4_mod_table$name)
mr4_data$scale <- (1/sqrt(mr4_data[,"vi"]))
mr4_mod_table$K <- as.vector(by(mr4_data, mr4_data[,"moderator"], function(x) length(x[,"yi"])))
mr4_group_no <- nrow(mr4_mod_table)

image_male <- readPNG(here("images/Male_icon.png"))
image_female <- readPNG(here("images/Female_icon.png"))
image_both <- readPNG(here("images/Both_sex_icon.png"))

# creating orchaRd plot
# for Figure 2

e <- ggplot(data = mr4_mod_table, aes(x = estimate, y = name)) + 
    scale_x_continuous(limits = c(-0.37, 1), breaks = seq(-0.4, 1, by = 0.2), labels = c("-0.4", "-0.2", "0.0", "0.2", "0.4", "0.6", "0.8", "1.0")) +  
    ggbeeswarm::geom_quasirandom(data = mr4_data, aes(x = yi_ICC, y = moderator, size = scale, colour=moderator), groupOnX=FALSE, alpha = 0.4) +
    geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = F, size = 0.5, alpha = 0.6) + # CI
    geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend = F, size = 1.2) + 
    geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
    geom_point(aes(fill=name), size = 3, shape = 21) + 
    annotate('text', x = 0.93, y = (seq(1, mr4_group_no, 1)+0.35), label= paste("italic(k)==", mr4_mod_table$K), parse=TRUE, hjust = "left", size=3.5) +
    ggplot2::theme_bw() +
    ggplot2::guides(fill="none", colour="none") +
    ggplot2::theme(axis.text.y = element_text(size=9, colour="black", hjust=0.5, angle=90),
                   legend.position = "none", axis.title.y = element_blank()) +
    ggplot2::labs(x=paste("Effect size (ICC)")) +
    scale_y_discrete(labels=c("F" = "Female", "M" = "Male", "B" = "Both")) +
              scale_fill_manual(values=cbpl) +
          scale_colour_manual(values=cbpl) +
        annotation_custom(grid::rasterGrob(image_male), xmin = -0.45, xmax = -0.25, ymin = 2.7, ymax = 3.3) +
    annotation_custom(grid::rasterGrob(image_female), xmin = -0.45, xmax = -0.25, ymin = 1.7, ymax = 2.3) +
    annotation_custom(grid::rasterGrob(image_both), xmin = -0.45, xmax = -0.25, ymin = 0.7, ymax = 1.3)

e

Figure 2e. Repeatability of avian migration timing for sex. The plot shows the group-wise means with 95% confidence intervals (thick lines) and 95% prediction intervals (thin lines), observed effect sizes (back-transformed to ICC) scaled by precision (circles) and k = number of effect sizes.

The effect of ecological group

meta_regression3 <- rma.mv(yi = Zr2, V = VCV, mods = ~taxa, random = list(~1 | es_ID,
    ~1 | paper_ID, ~1 | cohort_ID, ~1 | species_ID, ~1 | phylogeny), R = list(phylogeny = varcor),
    data = df)

# reordering df$taxa <- factor(df$taxa, levels = c('Waterbird', 'Seabird',
# 'Landbird'))

meta_regression3b <- rma.mv(yi = Zr2, V = VCV, mods = ~relevel(taxa, ref = "Waterbird"),
    random = list(~1 | es_ID, ~1 | paper_ID, ~1 | cohort_ID, ~1 | species_ID, ~1 |
        phylogeny), R = list(phylogeny = varcor), data = df)

# Orchard plot - need meta-regression without intercept
meta_regression3c <- rma.mv(yi = Zr2, V = VCV, mods = ~taxa - 1, random = list(~1 |
    es_ID, ~1 | paper_ID, ~1 | cohort_ID, ~1 | species_ID, ~1 | phylogeny), R = list(phylogeny = varcor),
    data = df)

Table S7

Regression coefficients (Estimate) and 95% confidence intervals (CIs) both in Zr and back-transformed to ICC from the meta-regression with taxa. Note that mu means the group mean while beta represents the contrast between two groups in the Unit column. R2marginal = 6.7%.

# getting marginal R2
r2_meta_regression3 <- r2_ml(meta_regression3)

# getting estimates including back-transformation to ICC

mr3 <- mod_results(meta_regression3c, mod = "taxa")
mr3_mod_table <- mr3$mod_table

mr3_data <- mr3$data

# calculate k for each method separately

# df %>% group_by(taxa) %>% summarise(mean(k))

mr3_data <- mr3_data %>%
    mutate(k = case_when(moderator == "Landbird" ~ 2.68, moderator == "Seabird" ~
        2.38, moderator == "Waterbird" ~ 3.08))

mr3_data$yi_ICC <- Zr_to_ICC(mr3_data$yi, mr3_data$k)

mr3_mod_table$k <- c(2.68, 2.38, 3.08)

for (i in names(mr3_mod_table)[2:6]) {

    mr3_mod_table[i] <- Zr_to_ICC(mr3_mod_table[i], mr3_mod_table$k)

}

# creating a table
tibble(`Fixed effect` = c(rep(as.character(mr3_mod_table$name), 2), cont_gen(mr3_mod_table$name)),
    Unit = c(rep(c("Zr (mu)", "ICC (mu)"), each = 3), rep("Zr (beta)", 3)), Estimate = c(meta_regression3c$b,
        mr3_mod_table$estimate, meta_regression3$b[-1], meta_regression3b$b[-(1:2)]),
    `Lower CI [0.025]` = c(meta_regression3c$ci.lb, mr3_mod_table$lowerCL, meta_regression3$ci.lb[-1],
        meta_regression3b$ci.lb[-(1:2)]), `Upper CI  [0.975]` = c(meta_regression3c$ci.ub,
        mr3_mod_table$upperCL, meta_regression3$ci.ub[-1], meta_regression3b$ci.ub[-(1:2)])) ->
    t_method
# `R2` = c(r2_meta_regression3[1], rep(NA,8))) -> t_method

t_method %>%
    kable("html", digits = 3) %>%
    kable_styling("striped", position = "left")
Fixed effect Unit Estimate Lower CI [0.025] Upper CI [0.975]
Landbird Zr (mu) 0.424 0.263 0.585
Seabird Zr (mu) 0.638 0.472 0.804
Waterbird Zr (mu) 0.564 0.405 0.722
Landbird ICC (mu) 0.333 0.205 0.454
Seabird ICC (mu) 0.520 0.398 0.626
Waterbird ICC (mu) 0.404 0.289 0.513
Landbird-Seabird Zr (beta) 0.214 -0.017 0.445
Landbird-Waterbird Zr (beta) 0.140 -0.086 0.366
Seabird-Waterbird Zr (beta) 0.074 -0.155 0.304

Figure 2d

mr3_data$moderator <- factor(mr3_data$moderator, levels = mr3_mod_table$name, labels = mr3_mod_table$name)
mr3_data$scale <- (1/sqrt(mr3_data[,"vi"]))
mr3_mod_table$K <- as.vector(by(mr3_data, mr3_data[,"moderator"], function(x) length(x[,"yi"])))
mr3_group_no <- nrow(mr3_mod_table)

image_waterbird <- readPNG(here("images/Waterbird_icon.png"))
image_seabird <- readPNG(here("images/Seabird_icon.png"))
image_landbird <- readPNG(here("images/Landbird_icon.png"))

# creating orchaRd plot

fig_eco_group <- ggplot(data = mr3_mod_table, aes(x = estimate, y = name)) + 
    scale_x_continuous(limits = c(-0.37, 1), breaks = seq(-0.4, 1, by = 0.2), labels = c("-0.4", "-0.2", "0.0", "0.2", "0.4", "0.6", "0.8", "1.0")) + 
    ggbeeswarm::geom_quasirandom(data = mr3_data, aes(x = yi_ICC, y = moderator, size = scale, colour=moderator), groupOnX=FALSE, alpha = 0.4) +
    geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = F, size = 0.5, alpha = 0.6) + # CI
    geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend = F, size = 1.2) + 
    geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
    geom_point(aes(fill=name), size = 3, shape = 21) + 
    annotate('text', x = 0.93, y = (seq(1, mr3_group_no, 1)+0.35), label= paste("italic(k)==", mr3_mod_table$K), parse=TRUE, hjust = "left", size=3.5) +
    ggplot2::theme_bw() +
    ggplot2::guides(fill="none", colour="none") +
    ggplot2::theme(axis.text.y = element_text(size=9, colour="black", hjust=0.5, angle=90),
                   legend.position = "none", axis.title.y = element_blank(), axis.text.x=element_text(size=9)) +
    # setting colours
  scale_color_manual(values = c("Seabird" = "#009E73",  "Landbird" = "#D55E00",  "Waterbird"= "#0072B2")) +
  scale_fill_manual(values = c("Seabird" = "#009E73",  "Landbird" = "#D55E00",  "Waterbird"= "#0072B2")) +
    annotation_custom(grid::rasterGrob(image_waterbird), xmin = -0.45, xmax = -0.25, ymin = 2.7, ymax = 3.3) +
    annotation_custom(grid::rasterGrob(image_seabird), xmin = -0.45, xmax = -0.25, ymin = 1.7, ymax = 2.3) +
    annotation_custom(grid::rasterGrob(image_landbird), xmin = -0.45, xmax = -0.25, ymin = 0.7, ymax = 1.3) +
  labs(x="Effect size (ICC)")

fig_eco_group

# for Figure 2

d <- ggplot(data = mr3_mod_table, aes(x = estimate, y = name)) + 
    scale_x_continuous(limits = c(-0.37,1), breaks = seq(-0.4, 1, by = 0.2), labels = c("-0.4","-0.2", "0.0", "0.2", "0.4", "0.6", "0.8", "1.0")) + 
    ggbeeswarm::geom_quasirandom(data = mr3_data, aes(x = yi_ICC, y = moderator, size = scale, colour=moderator), groupOnX=FALSE, alpha = 0.4) +
    geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = F, size = 0.5, alpha = 0.6) + # CI
    geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend = F, size = 1.2) + 
    geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.5) + # creating dots and different size (bee-swarm and bubbles)
    geom_point(aes(fill=name), size = 3, shape = 21) + 
    #ggplot2::annotate("text", x = max(mr2_data$yi_ICC) + (max(mr2_data$yi_ICC)*0.10), y = (seq(1, mr2_group_no, 1)+0.3),label = paste("italic(k)==", mr2_mod_table$K), parse = TRUE, hjust = "right", size = 3.5) +
    annotate('text', x = 0.93, y = (seq(1, mr3_group_no, 1)+0.35), label= paste("italic(k)==", mr3_mod_table$K), parse=TRUE, hjust = "left", size=3.5) +
    ggplot2::theme_bw() +
    ggplot2::guides(fill="none", colour="none") +
    ggplot2::theme(axis.text.y = element_text(size=9, colour="black", hjust=0.5, angle=90),
                   legend.position = "none", axis.title = element_blank()) +
    # setting colours
  scale_color_manual(values = c("Seabird" = "#009E73",  "Landbird" = "#D55E00",  "Waterbird"= "#0072B2")) +
  scale_fill_manual(values = c("Seabird" = "#009E73",  "Landbird" = "#D55E00",  "Waterbird"= "#0072B2")) +
    annotation_custom(grid::rasterGrob(image_waterbird), xmin = -0.45, xmax = -0.25, ymin = 2.7, ymax = 3.3) +
    annotation_custom(grid::rasterGrob(image_seabird), xmin = -0.45, xmax = -0.25, ymin = 1.7, ymax = 2.3) +
    annotation_custom(grid::rasterGrob(image_landbird), xmin = -0.45, xmax = -0.25, ymin = 0.7, ymax = 1.3)

Figure 2d. Repeatability of avian migration timing for ecological group. The plot shows the group-wise means with 95% confidence intervals (thick lines) and 95% prediction intervals (thin lines), observed effect sizes (back-transformed to ICC) scaled by precision (circles) and k = number of effect sizes.

Putting together Figure 2

# building fig 2 using patchwork

# Figure2 <- (a / b / c / d / e + plot_layout(heights = c(1.8, 3.7, 3, 3, 3)) +
# plot_annotation(tag_levels = 'a', tag_suffix = ')'))

# Figure2

# ggsave(here('figs/Figure_2_R_zero.pdf'), width = 180, height = 300,
# units=c('mm'), dpi=600) edited this file in Affinity Designer

Figure 2. Putting all five panels together: Figure 2a - Figure 2e (see the main text). Note this plot was edited in Affinity Designer after construction in R.

The effect of number of observations per individual (k)

meta_regression5 <- rma.mv(yi = Zr2, V = VCV, mods = ~k, random = list(~1 | es_ID,
    ~1 | paper_ID, ~1 | cohort_ID, ~1 | species_ID, ~1 | phylogeny), R = list(phylogeny = varcor),
    data = df)

Table S8

Regression coefficients (Estimate) and 95% confidence intervals (CIs) in Zr from the meta-regression with k (number of observations per individual). R2marginal = 0.2%.

# getting marginal R2
r2_meta_regression5 <- r2_ml(meta_regression5)

# creating a table
tibble(`Fixed effect` = c("Intercept", "k"), Estimate = c(meta_regression5$b), `Lower CI [0.025]` = c(meta_regression5$ci.lb),
    `Upper CI  [0.975]` = c(meta_regression5$ci.ub)) -> t_k
# `R2` = c(r2_meta_regression5[1], NA)) -> t_k

t_k %>%
    kable("html", digits = 3) %>%
    kable_styling("striped", position = "left")
Fixed effect Estimate Lower CI [0.025] Upper CI [0.975]
Intercept 0.563 0.358 0.769
k -0.011 -0.062 0.041

Figure S4

pred_meta_regression5 <- predict.rma(meta_regression5)

# creating bubble plot
df %>%
    mutate(fit = pred_meta_regression5$pred, ci.lb = pred_meta_regression5$ci.lb,
        ci.ub = pred_meta_regression5$ci.ub, pr.lb = pred_meta_regression5$cr.lb,
        pr.ub = pred_meta_regression5$cr.ub) %>%
    ggplot(aes(x = k, y = Zr2)) + geom_point(aes(size = (1/sqrt(VZr))), shape = 21,
    alpha = 0.5, fill = "grey85", colour = "grey60", col = "gray25", stroke = 1) +
    geom_line(aes(y = fit), size = 1.5, colour = "darkorchid4") + geom_ribbon(aes(ymin = ci.lb,
    ymax = ci.ub, color = NULL), alpha = 0.3, fill = "darkorchid4") + labs(x = "Number of observations per individual (k)",
    y = "Effect size (Zr)", size = "Precison (1/SE)") + theme_bw() + scale_size_continuous(range = c(1,
    12)) + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
    theme(text = element_text(size = 9, colour = "black", hjust = 0.5), legend.text = element_text(size = 8),
        legend.position = c(1, 1), legend.justification = c(1, 1), legend.background = element_blank(),
        legend.direction = "horizontal", legend.title = element_text(size = 8))

Figure S4. Repeatability of avian migration timing for the continuous variable k, where the solid line represents the model estimate and the shading shows the 95% confidence intervals, with individual data points scaled by precision (1/SE).

df2 <- df %>% filter(k < 10)
VCV2 <- impute_covariance_matrix(vi = df2$VZr, cluster = df2$cohort_ID, r = 0.5)

meta_regression6 <- rma.mv(yi = Zr2, V = VCV2, 
                           mods = ~ k,
                           random = list(~1 | es_ID, 
                                         ~1 | paper_ID, 
                                         ~1 | cohort_ID, 
                                         ~1 | species_ID, 
                                         ~1 | phylogeny),
                           R = list(phylogeny = varcor), # added in phylogney
                           data = df2)

When we remove the two points with high k values (k = 12.4), the results are similar. There is no significant effect (slope = 0.087, 95% CI = [-0.012, 0.187]; R2marginal = 4.4%) of ‘k’ on effect sizes, showing that repeatability does not vary with the number of observations per individual.

Model selection (multi-predictor model)

Here we used the MuMin package to generate all possible moderator combinations (using all five variables: annual_event, method, sex, taxa & k (number of observations per individuals)), determine the importance of the moderators, and generate model-averaged estimates.

eval(metafor:::.MuMIn)

full_model_MuMIn <- rma.mv(yi = Zr2, V = VCV, 
                          mods = ~ method + 
                            taxa + 
                            sex + 
                            annual_event +
                            k, 
                          random = list(~1 | es_ID, 
                                        ~1 | paper_ID, 
                                        ~1 | cohort_ID, 
                                        ~1 | species_ID, 
                                        ~1 | phylogeny),
                          R = list(phylogeny = varcor), 
                          method = "ML", # maximum likelihood for model selection
                          data = df)

# vif.rma(full_model_MuMIn)  # No major problems of collinearity (VIF <4)

candidate_models<-dredge(full_model_MuMIn) # Generate all possible combinations of moderators

candidates_aic2 <- subset(candidate_models, delta<=2) # Display all models within 2 values of AICc

importance <- sw(model.avg(candidate_models, subset=delta<=2))# relative importance (sum of weights) of the moderators

mod.avg <- summary(model.avg(candidate_models, subset=delta<=2)) # Generate model-averaged estimates 

confidence <- confint(mod.avg, full=TRUE) # Generate confidence intervals for the estimates averaged using full-averages procedures

Table S9

The top five models within the \(\Delta\)AIC difference of 2, and which five variables: annual_event, method, taxa, sex, & k were included (indicated by \(+\)); model weights and the sum of weights for each of the variables are included.

# creating a table
tibble(`Model (variable weight)` = c("Model1", "Model2", "Model3", "Model4", "Model5",
    "(Sum of weights)"), annual_event = c(if_else(candidates_aic2$annual_event ==
    "+", "$+$", "NA"), round(importance[1], 3)), taxa = c(if_else(candidates_aic2$taxa ==
    "+", "$+$", "NA"), round(importance[2], 3)), method = c(if_else(candidates_aic2$method ==
    "+", "$+$", "NA"), round(importance[3], 3)), k = c(if_else(candidates_aic2$k <=
    0, "$+$", "NA"), round(importance[4], 3)), sex = c(if_else(candidates_aic2$sex ==
    "+", "$+$", "NA"), round(importance[5], 3)), delta_AICc = c(candidates_aic2$delta,
    NA), Weight = c(candidates_aic2$weight, NA)) %>%
    kable("html", digits = 3) %>%
    kable_styling("striped", position = "left")
Model (variable weight) annual_event taxa method k sex delta_AICc Weight
Model1 \(+\) \(+\) NA NA NA 0.000 0.288
Model2 \(+\) NA NA NA NA 0.353 0.242
Model3 \(+\) NA \(+\) NA NA 0.474 0.227
Model4 \(+\) \(+\) NA \(+\) NA 1.512 0.135
Model5 \(+\) NA NA NA \(+\) 1.978 0.107
(Sum of weights) 1 0.424 0.227 0.135 0.107 NA NA

Model averaging

Table S10

The average estimates for regression coefficients (Estimate) and 95% confidence intervals (CIs) from the model averaging procedure using full-averages (assuming zero values for moderators when they do not occur).

# creating a table
tibble(`Fixed effect` = c("Intercept", "Depart_breed", "Nonbreed_arrival", "Nonbreed_depart",
    "Female", "Male", "Seabird", "Waterbird", "GLS", "Satellite", "k"), Estimate = mod.avg$coefmat.full[,
    1], `Lower CI [0.025]` = confidence[, 1], `Upper CI  [0.975]` = confidence[,
    2]) %>%
    kable("html", digits = 3) %>%
    kable_styling("striped", position = "left")
Fixed effect Estimate Lower CI [0.025] Upper CI [0.975]
Intercept 0.437 0.251 0.624
Depart_breed -0.090 -0.236 0.056
Nonbreed_arrival 0.110 -0.063 0.282
Nonbreed_depart 0.241 0.081 0.402
Female 0.108 -0.177 0.393
Male 0.039 -0.126 0.204
Seabird 0.044 -0.139 0.227
Waterbird 0.044 -0.155 0.244
GLS -0.003 -0.026 0.020
Satellite -0.012 -0.108 0.085
k -0.019 -0.143 0.105

Sensitivity Analysis

Variance-covariance matrix with different levels of correlation

Multiple repeatability estimates were measured on the same animals within a paper (cohort ID) which induces a correlation between sampling error variances (Noble et al., 2017). Thus, we constructed variance-covariance matrices to model shared sampling error for effect sizes from the same cohort. We initially assumed a 0.5 correlation, but also ran the phylogenetic meta-analytic model with a 0.25 and 0.75 correlation. All three correlations yielded qualitatively similar results, thus throughout the manuscript we assume a 0.5 correlation, but the results for the other correlation values are presented below.

Table S11

Overall effects (meta-analytic means) and 95% confidence intervals (CIs) in Zr and heterogeneity, I2, for the phylogenetic multilevel intercept-only meta-analysis model when testing different levels of correlation (r = 0.25, 0.50, and 0.75) between sampling variances from the same cohort of birds.

# Create a variance-covariance matrix at the cohort level with different correlation values
VCV_25 <- impute_covariance_matrix(vi = df$VZr, cluster = df$cohort_ID, r = 0.25)
VCV_75 <- impute_covariance_matrix(vi = df$VZr, cluster = df$cohort_ID, r = 0.75)

# Run phylogenetic meta-analytic models with 0.25 and 0.75 VCV
ma_model2_VCV25 <- rma.mv(yi = Zr2, V = VCV_25, 
                      random = list(~1 | es_ID, 
                                    ~1 | paper_ID, 
                                    ~1 | cohort_ID, 
                                    ~1 | species_ID, # non-phylo effect
                                    ~1 | phylogeny), # phylo effect
                      R = list(phylogeny = varcor), # phylogenetic relatedness
                      data = df)

i2_ma2_VCV25 <- round(i2_ml(ma_model2_VCV25)*100,1)

ma_model2_VCV75 <- rma.mv(yi = Zr2, V = VCV_75, 
                      random = list(~1 | es_ID, 
                                    ~1 | paper_ID, 
                                    ~1 | cohort_ID, 
                                    ~1 | species_ID, # non-phylo effect
                                    ~1 | phylogeny), # phylo effect
                      R = list(phylogeny = varcor), # phylogenetic relatedness
                      data = df)

i2_ma2_VCV75 <- round(i2_ml(ma_model2_VCV75)*100,1)

# creating a table
tibble(
  Model = c("Meta-analysis, r=0.25", "Meta-analysis, r=0.50", "Meta-analysis, r=0.75"),
  `Overall mean` = c(ma_model2_VCV25$b, ma_model2$b, ma_model2_VCV75$b),
  `Lower CI [0.025]` = c(ma_model2_VCV25$ci.lb, ma_model2$ci.lb, ma_model2_VCV75$ci.lb),
  `Upper CI [0.975]` = c(ma_model2_VCV25$ci.ub, ma_model2$ci.ub, ma_model2_VCV75$ci.ub),
  `I^2^~total~` = c(i2_ma2_VCV25[1], i2_ma2[1], i2_ma2_VCV75[1]),
  `I^2^~es~` = c(i2_ma2_VCV25[2], i2_ma2[2], i2_ma2_VCV75[2]),
  `I^2^~paper~` = c(i2_ma2_VCV25[3], i2_ma2[3], i2_ma2_VCV75[3]),
  `I^2^~cohort~` = c(i2_ma2_VCV25[4], i2_ma2[4], i2_ma2_VCV75[4]),
  `I^2^~species~` = c(i2_ma2_VCV25[5], i2_ma2[5], i2_ma2_VCV75[5]),
  `I^2^~phylo~` = c(i2_ma2_VCV25[6], i2_ma2[6], i2_ma2_VCV75[6]),) %>% 
  kable("html",  digits = 3) %>%
  kable_styling("striped", position = "left")
Model Overall mean Lower CI [0.025] Upper CI [0.975] I2total I2es I2paper I2cohort I2species I2phylo
Meta-analysis, r=0.25 0.535 0.405 0.664 82.8 40.6 0 0 34.3 7.8
Meta-analysis, r=0.50 0.532 0.400 0.664 84.2 49.7 0 0 27.3 7.2
Meta-analysis, r=0.75 0.530 0.396 0.663 86.2 60.5 0 0 19.6 6.1

Inclusion of negative repeatabiliy estimates

Correlation- and ANOVA-based repeatabilities can produce negative values, often reflecting noise around a statistical zero (Nakagawa & Schielzeth, 2010). For our main analyses, we set these negative estimates to zero, however here, we re-ran all the meta-analytic and meta-regression models with these negative values included.

Table S12

Overall effects (meta-analytic means) and 95% confidence intervals (CIs) both in Zr and back-transformed to ICC, and heterogeneity, I2, for the multilevel intercept-only meta-analysis models including and excluding phylogeny when negative repeatability values are included.

ma_model1_neg <- rma.mv(yi = Zr, V = VCV,
                    random = list(~1 | es_ID, 
                                  ~1 | paper_ID, 
                                  ~1 | cohort_ID, 
                                  ~1 | species_ID), 
                    data = df)

ma_model2_neg <- rma.mv(yi = Zr, V = VCV, 
                    random = list(~1 | es_ID, 
                                  ~1 | paper_ID, 
                                  ~1 | cohort_ID, 
                                  ~1 | species_ID, # non-phylo effect
                                  ~1 | phylogeny), # phylo effect
                    R = list(phylogeny = varcor), # phylogenetic relatedness
                    data = df)

## # estimating I2 as measure of heterogeneity
i2_ma1_neg <- round(i2_ml(ma_model1_neg)*100,1)
i2_ma2_neg <- round(i2_ml(ma_model2_neg)*100,1)

# Back-transform to ICC
ma1_neg <- mod_results(ma_model1_neg, mod="Int")
ma1_mod_table_neg <- ma1_neg$mod_table

ma2_neg <- mod_results(ma_model2_neg, mod="Int")
ma2_mod_table_neg <- ma2_neg$mod_table

# need to calculate k for whole data set to use in formula

k_all <- mean(df$k)

for(i in names(ma1_mod_table_neg)[2:6]){
  
  ma1_mod_table_neg[i] <- Zr_to_ICC(ma1_mod_table_neg[i], k_all)
  
}

for(i in names(ma2_mod_table_neg)[2:6]){
  
  ma2_mod_table_neg[i] <- Zr_to_ICC(ma2_mod_table_neg[i], k_all)
  
}

# creating a table
tibble(
  Model = c("Meta-analysis (Zr)", "Meta-analysis (ICC)", "Meta-analysis phylo (Zr)", "Meta-analysis phylo (ICC)"),
  `Overall mean` = c(ma_model1_neg$b, ma1_mod_table_neg$estimate, ma_model2_neg$b, ma2_mod_table_neg$estimate),
  `Lower CI [0.025]` = c(ma_model1_neg$ci.lb, ma1_mod_table_neg$lowerCL, ma_model2_neg$ci.lb, ma2_mod_table_neg$lowerCL),
  `Upper CI [0.975]` = c(ma_model1_neg$ci.ub, ma1_mod_table_neg$upperCL, ma_model2_neg$ci.ub, ma2_mod_table_neg$upperCL),
  `I^2^~total~` = c(i2_ma1_neg[1], NA, i2_ma2_neg[1], NA),
  `I^2^~es~` = c(i2_ma1_neg[2], NA, i2_ma2_neg[2], NA),
  `I^2^~paper~` = c(i2_ma1_neg[3], NA, i2_ma2_neg[3], NA),
  `I^2^~cohort~` = c(i2_ma1_neg[4], NA, i2_ma2_neg[4], NA),
  `I^2^~species~` = c(i2_ma1_neg[5], NA, i2_ma2_neg[5], NA),
  `I^2^~phylo~` = c(NA, NA, i2_ma2_neg[6], NA),) %>% 
  kable("html",  digits = 3) %>%
  kable_styling("striped", position = "left")
Model Overall mean Lower CI [0.025] Upper CI [0.975] I2total I2es I2paper I2cohort I2species I2phylo
Meta-analysis (Zr) 0.529 0.429 0.629 85.3 51.2 0 0 34.2 NA
Meta-analysis (ICC) 0.412 0.336 0.484 NA NA NA NA NA NA
Meta-analysis phylo (Zr) 0.524 0.397 0.650 85.5 50.6 0 0 30.2 4.8
Meta-analysis phylo (ICC) 0.408 0.311 0.498 NA NA NA NA NA NA 
# meta-regression: mutiple intercepts
meta_regression1_neg <- rma.mv(yi = Zr, V = VCV, 
                           mods = ~ annual_event,
                           random = list(~1 | es_ID, 
                                         ~1 | paper_ID, 
                                         ~1 | cohort_ID, 
                                         ~1 | species_ID, 
                                         ~1 | phylogeny),
                           R = list(phylogeny = varcor), # added in phylogney
                           data = df)

meta_regression1b_neg <- rma.mv(yi = Zr, V = VCV, 
                            mods = ~ relevel(annual_event, ref = "Depart_breed"),
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor), # added in phylogney
                            data = df)


meta_regression1c_neg <- rma.mv(yi = Zr, V = VCV, 
                            mods = ~ relevel(annual_event, ref = "Nonbreed_depart"),
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor), # added in phylogney
                            data = df)

# meta-regression: contrast (for orchard plot)
meta_regression1d_neg <- rma.mv(yi = Zr, V = VCV, 
                            mods = ~ annual_event -1,
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor), # added in phylogney
                            data = df)

Table S13

Regression coefficients (Estimate) and 95% confidence intervals (CIs) both in Zr and back-transformed to ICC from the meta-regression with annual_event when negative repeatability values are included. Note that mu means the group mean while beta represents the contrast between two groups in the Unit column. R2marginal = 10.9%.

# getting marginal R2
r2_meta_regression1_neg <- r2_ml(meta_regression1_neg)

# getting estimates including back-transformation to ICC

mr1_neg <- mod_results(meta_regression1d_neg, mod = "annual_event")
mr1_mod_table_neg <- mr1_neg$mod_table

mr1_data_neg <- mr1_neg$data

# calculate k for each method separately

# df %>% group_by(annual_event) %>% summarise(mean(k))

mr1_data_neg <- mr1_data_neg %>%
    mutate(k = case_when(moderator == "Arrival_breed" ~ 2.55, moderator == "Depart_breed" ~
        2.45, moderator == "Nonbreed_arrival" ~ 3.2, moderator == "Nonbreed_depart" ~
        2.94))

mr1_data_neg$yi_ICC <- Zr_to_ICC(mr1_data_neg$yi, mr1_data_neg$k)

mr1_mod_table_neg$k <- c(2.55, 2.45, 3.2, 2.94)

for (i in names(mr1_mod_table_neg)[2:6]) {

    mr1_mod_table_neg[i] <- Zr_to_ICC(mr1_mod_table_neg[i], mr1_mod_table_neg$k)

}

# creating a table
tibble(`Fixed effect` = c(rep(as.character(mr1_mod_table_neg$name), 2), cont_gen(mr1_mod_table_neg$name)),
    Unit = c(rep(c("Zr (mu)", "ICC (mu)"), each = 4), rep("Zr (beta)", 6)), Estimate = c(meta_regression1d_neg$b,
        mr1_mod_table_neg$estimate, meta_regression1_neg$b[-1], meta_regression1b_neg$b[-(1:2)],
        meta_regression1c_neg$b[-(1:3)]), `Lower CI [0.025]` = c(meta_regression1d_neg$ci.lb,
        mr1_mod_table_neg$lowerCL, meta_regression1_neg$ci.lb[-1], meta_regression1b_neg$ci.lb[-(1:2)],
        meta_regression1c_neg$ci.lb[-(1:3)]), `Upper CI  [0.975]` = c(meta_regression1d_neg$ci.ub,
        mr1_mod_table_neg$upperCL, meta_regression1_neg$ci.ub[-1], meta_regression1b_neg$ci.ub[-(1:2)],
        meta_regression1c_neg$ci.ub[-(1:3)])) -> t_annual_event_neg
# `R2` = c(r2_meta_regression1[1], rep(NA,13))) ->

t_annual_event_neg %>%
    kable("html", digits = 3) %>%
    kable_styling("striped", position = "left")
Fixed effect Unit Estimate Lower CI [0.025] Upper CI [0.975]
Arrival_breed Zr (mu) 0.477 0.314 0.640
Depart_breed Zr (mu) 0.352 0.168 0.536
Nonbreed_arrival Zr (mu) 0.592 0.394 0.790
Nonbreed_depart Zr (mu) 0.712 0.523 0.901
Arrival_breed ICC (mu) 0.385 0.256 0.505
Depart_breed ICC (mu) 0.294 0.140 0.440
Nonbreed_arrival ICC (mu) 0.414 0.272 0.546
Nonbreed_depart ICC (mu) 0.518 0.386 0.632
Arrival_breed-Depart_breed Zr (beta) -0.125 -0.273 0.022
Arrival_breed-Nonbreed_arrival Zr (beta) 0.114 -0.060 0.289
Arrival_breed-Nonbreed_depart Zr (beta) 0.235 0.071 0.399
Depart_breed-Nonbreed_arrival Zr (beta) 0.240 0.055 0.424
Depart_breed-Nonbreed_depart Zr (beta) 0.360 0.183 0.537
Nonbreed_arrival-Nonbreed_depart Zr (beta) -0.120 -0.301 0.060 
meta_regression2_neg <- rma.mv(yi = Zr, V = VCV, mods = ~method, random = list(~1 |
    es_ID, ~1 | paper_ID, ~1 | cohort_ID, ~1 | species_ID, ~1 | phylogeny), R = list(phylogeny = varcor),
    data = df)

# reordering df$method <- factor(df$method, levels = c('Conventional', 'GLS',
# 'Satellite'))

meta_regression2b_neg <- rma.mv(yi = Zr, V = VCV, mods = ~relevel(method, ref = "GLS"),
    random = list(~1 | es_ID, ~1 | paper_ID, ~1 | cohort_ID, ~1 | species_ID, ~1 |
        phylogeny), R = list(phylogeny = varcor), data = df)

# Orchard plot - need meta-regression without intercept
meta_regression2c_neg <- rma.mv(yi = Zr, V = VCV, mods = ~method - 1, random = list(~1 |
    es_ID, ~1 | paper_ID, ~1 | cohort_ID, ~1 | species_ID, ~1 | phylogeny), R = list(phylogeny = varcor),
    data = df)

Table S14

Regression coefficients (Estimate) and 95% confidence intervals (CIs) both in Zr and back-transformed to ICC from the meta-regression with method when negative repeatability values are included. Note that mu means the group mean while beta represents the contrast between two groups in the Unit column. R2marginal = 4.2%.

# getting marginal R2
r2_meta_regression2_neg <- r2_ml(meta_regression2_neg)

# getting estimates including back-transformation to ICC

mr2_neg <- mod_results(meta_regression2c_neg, mod = "method")
mr2_mod_table_neg <- mr2_neg$mod_table

mr2_data_neg <- mr2_neg$data

# calculate k for each method separately

# df %>% group_by(method) %>% summarise(mean(k))


mr2_data_neg <- mr2_data_neg %>%
    mutate(k = case_when(moderator == "GLS" ~ 2.2, moderator == "Conventional" ~
        3.13, moderator == "Satellite" ~ 3.28))

mr2_data_neg$yi_ICC <- Zr_to_ICC(mr2_data_neg$yi, mr2_data_neg$k)

mr2_mod_table_neg$k <- c(3.13, 2.2, 3.28)

for (i in names(mr2_mod_table_neg)[2:6]) {

    mr2_mod_table_neg[i] <- Zr_to_ICC(mr2_mod_table_neg[i], mr2_mod_table_neg$k)

}

# creating a table
tibble(`Fixed effect` = c(rep(as.character(mr2_mod_table_neg$name), 2), cont_gen(mr2_mod_table_neg$name)),
    Unit = c(rep(c("Zr (mu)", "ICC (mu)"), each = 3), rep("Zr (beta)", 3)), Estimate = c(meta_regression2c_neg$b,
        mr2_mod_table_neg$estimate, meta_regression2_neg$b[-1], meta_regression2b_neg$b[-(1:2)]),
    `Lower CI [0.025]` = c(meta_regression2c_neg$ci.lb, mr2_mod_table_neg$lowerCL,
        meta_regression2_neg$ci.lb[-1], meta_regression2b_neg$ci.lb[-(1:2)]), `Upper CI  [0.975]` = c(meta_regression2c_neg$ci.ub,
        mr2_mod_table_neg$upperCL, meta_regression2_neg$ci.ub[-1], meta_regression2b_neg$ci.ub[-(1:2)])) ->
    t_method_neg

t_method_neg %>%
    kable("html", digits = 3) %>%
    kable_styling("striped", position = "left")
Fixed effect Unit Estimate Lower CI [0.025] Upper CI [0.975]
Conventional Zr (mu) 0.429 0.278 0.579
GLS Zr (mu) 0.574 0.425 0.723
Satellite Zr (mu) 0.633 0.415 0.851
Conventional ICC (mu) 0.302 0.192 0.411
GLS ICC (mu) 0.494 0.379 0.596
Satellite ICC (mu) 0.437 0.283 0.577
Conventional-GLS Zr (beta) 0.145 -0.054 0.345
Conventional-Satellite Zr (beta) 0.204 -0.060 0.469
GLS-Satellite Zr (beta) 0.059 -0.205 0.323 
# reordering df$sex <- factor(df$sex, levels = c('F', 'M', 'B'))

meta_regression4_neg <- rma.mv(yi = Zr, V = VCV, mods = ~sex, random = list(~1 |
    es_ID, ~1 | paper_ID, ~1 | cohort_ID, ~1 | species_ID, ~1 | phylogeny), R = list(phylogeny = varcor),
    data = df)

meta_regression4b_neg <- rma.mv(yi = Zr, V = VCV, mods = ~relevel(sex, ref = "M"),
    random = list(~1 | es_ID, ~1 | paper_ID, ~1 | cohort_ID, ~1 | species_ID, ~1 |
        phylogeny), R = list(phylogeny = varcor), data = df)

# Orchard plot - need meta-regression without intercept
meta_regression4c_neg <- rma.mv(yi = Zr, V = VCV, mods = ~sex - 1, random = list(~1 |
    es_ID, ~1 | paper_ID, ~1 | cohort_ID, ~1 | species_ID, ~1 | phylogeny), R = list(phylogeny = varcor),
    data = df)

Table S15

Regression coefficients (Estimate) and 95% confidence intervals (CIs) both in Zr and back-transformed to ICC from the meta-regression with sex when negative repeatability values are included. Note that mu means the group mean while beta represents the contrast between two groups in the Unit column. R2marginal = 8%. B = both sexes, M = male, and F = female.

# getting marginal R2
r2_meta_regression4_neg <- r2_ml(meta_regression4_neg)

# getting estimates including back-transformation to ICC

mr4_neg <- mod_results(meta_regression4c_neg, mod = "sex")
mr4_mod_table_neg <- mr4_neg$mod_table

mr4_data_neg <- mr4_neg$data

# calculate k for each method separately

# df %>% group_by(sex) %>% summarise(mean(k))


mr4_data_neg <- mr4_data_neg %>%
    mutate(k = case_when(moderator == "F" ~ 2.37, moderator == "M" ~ 2.38, moderator ==
        "B" ~ 2.82))

mr4_data_neg$yi_ICC <- Zr_to_ICC(mr4_data_neg$yi, mr4_data_neg$k)

mr4_mod_table_neg$k <- c(2.37, 2.38, 2.82)

for (i in names(mr4_mod_table_neg)[2:6]) {

    mr4_mod_table_neg[i] <- Zr_to_ICC(mr4_mod_table_neg[i], mr4_mod_table_neg$k)

}

# creating a table
tibble(`Fixed effect` = c(rep(as.character(mr4_mod_table_neg$name), 2), cont_gen(mr4_mod_table_neg$name)),
    Unit = c(rep(c("Zr (mu)", "ICC (mu)"), each = 3), rep("Zr (beta)", 3)), Estimate = c(meta_regression4c_neg$b,
        mr4_mod_table_neg$estimate, meta_regression4_neg$b[-1], meta_regression4b_neg$b[-(1:2)]),
    `Lower CI [0.025]` = c(meta_regression4c_neg$ci.lb, mr4_mod_table_neg$lowerCL,
        meta_regression4_neg$ci.lb[-1], meta_regression4b_neg$ci.lb[-(1:2)]), `Upper CI  [0.975]` = c(meta_regression4c_neg$ci.ub,
        mr4_mod_table_neg$upperCL, meta_regression4_neg$ci.ub[-1], meta_regression4b_neg$ci.ub[-(1:2)])) ->
    t_sex_neg

t_sex_neg %>%
    kable("html", digits = 3) %>%
    kable_styling("striped", position = "left")
Fixed effect Unit Estimate Lower CI [0.025] Upper CI [0.975]
B Zr (mu) 0.602 0.489 0.715
F Zr (mu) 0.450 0.239 0.661
M Zr (mu) 0.347 0.166 0.527
B ICC (mu) 0.496 0.412 0.573
F ICC (mu) 0.380 0.205 0.536
M ICC (mu) 0.262 0.122 0.399
B-F Zr (beta) -0.153 -0.384 0.079
B-M Zr (beta) -0.256 -0.462 -0.050
F-M Zr (beta) 0.103 -0.141 0.347 
meta_regression3_neg <- rma.mv(yi = Zr, V = VCV, mods = ~taxa, random = list(~1 |
    es_ID, ~1 | paper_ID, ~1 | cohort_ID, ~1 | species_ID, ~1 | phylogeny), R = list(phylogeny = varcor),
    data = df)

# reordering df$taxa <- factor(df$taxa, levels = c('Waterbird', 'Seabird',
# 'Landbird'))

meta_regression3b_neg <- rma.mv(yi = Zr, V = VCV, mods = ~relevel(taxa, ref = "Waterbird"),
    random = list(~1 | es_ID, ~1 | paper_ID, ~1 | cohort_ID, ~1 | species_ID, ~1 |
        phylogeny), R = list(phylogeny = varcor), data = df)

# Orchard plot - need meta-regression without intercept
meta_regression3c_neg <- rma.mv(yi = Zr, V = VCV, mods = ~taxa - 1, random = list(~1 |
    es_ID, ~1 | paper_ID, ~1 | cohort_ID, ~1 | species_ID, ~1 | phylogeny), R = list(phylogeny = varcor),
    data = df)

Table S16

Regression coefficients (Estimate) and 95% confidence intervals (CIs) both in Zr and back-transformed to ICC from the meta-regression with taxa when negative repeatability values are included. Note that mu means the group mean while beta represents the contrast between two groups in the Unit column. R2marginal = 5.1%.

# getting marginal R2
r2_meta_regression3_neg <- r2_ml(meta_regression3_neg)

# getting estimates including back-transformation to ICC

mr3_neg <- mod_results(meta_regression3c_neg, mod = "taxa")
mr3_mod_table_neg <- mr3_neg$mod_table

mr3_data_neg <- mr3_neg$data

# calculate k for each method separately

# df %>% group_by(taxa) %>% summarise(mean(k))

mr3_data_neg <- mr3_data_neg %>%
    mutate(k = case_when(moderator == "Landbird" ~ 2.68, moderator == "Seabird" ~
        2.38, moderator == "Waterbird" ~ 3.08))

mr3_data_neg$yi_ICC <- Zr_to_ICC(mr3_data_neg$yi, mr3_data_neg$k)

mr3_mod_table_neg$k <- c(2.68, 2.38, 3.08)

for (i in names(mr3_mod_table_neg)[2:6]) {

    mr3_mod_table_neg[i] <- Zr_to_ICC(mr3_mod_table_neg[i], mr3_mod_table_neg$k)

}

# creating a table
tibble(`Fixed effect` = c(rep(as.character(mr3_mod_table_neg$name), 2), cont_gen(mr3_mod_table_neg$name)),
    Unit = c(rep(c("Zr (mu)", "ICC (mu)"), each = 3), rep("Zr (beta)", 3)), Estimate = c(meta_regression3c_neg$b,
        mr3_mod_table_neg$estimate, meta_regression3_neg$b[-1], meta_regression3b_neg$b[-(1:2)]),
    `Lower CI [0.025]` = c(meta_regression3c_neg$ci.lb, mr3_mod_table_neg$lowerCL,
        meta_regression3_neg$ci.lb[-1], meta_regression3b_neg$ci.lb[-(1:2)]), `Upper CI  [0.975]` = c(meta_regression3c_neg$ci.ub,
        mr3_mod_table_neg$upperCL, meta_regression3_neg$ci.ub[-1], meta_regression3b_neg$ci.ub[-(1:2)])) ->
    t_method_neg

t_method_neg %>%
    kable("html", digits = 3) %>%
    kable_styling("striped", position = "left")
Fixed effect Unit Estimate Lower CI [0.025] Upper CI [0.975]
Landbird Zr (mu) 0.417 0.247 0.587
Seabird Zr (mu) 0.612 0.438 0.786
Waterbird Zr (mu) 0.562 0.394 0.729
Landbird ICC (mu) 0.327 0.193 0.454
Seabird ICC (mu) 0.502 0.371 0.616
Waterbird ICC (mu) 0.403 0.280 0.517
Landbird-Seabird Zr (beta) 0.195 -0.048 0.438
Landbird-Waterbird Zr (beta) 0.145 -0.094 0.383
Seabird-Waterbird Zr (beta) 0.050 -0.191 0.292 
meta_regression5_neg <- rma.mv(yi = Zr, V = VCV, mods = ~k, random = list(~1 | es_ID,
    ~1 | paper_ID, ~1 | cohort_ID, ~1 | species_ID, ~1 | phylogeny), R = list(phylogeny = varcor),
    data = df)

Table S17

Regression coefficients (Estimate) and 95% confidence intervals (CIs) in Zr from the meta-regression with k (number of observations per individual) when negative repeatability values are included. R2marginal = 0.1%.

# getting marginal R2
r2_meta_regression5_neg <- r2_ml(meta_regression5_neg)

# creating a table
tibble(`Fixed effect` = c("Intercept", "k"), Estimate = c(meta_regression5_neg$b),
    `Lower CI [0.025]` = c(meta_regression5_neg$ci.lb), `Upper CI  [0.975]` = c(meta_regression5_neg$ci.ub)) ->
    t_k_neg

t_k_neg %>%
    kable("html", digits = 3) %>%
    kable_styling("striped", position = "left")
Fixed effect Estimate Lower CI [0.025] Upper CI [0.975]
Intercept 0.542 0.336 0.749
k -0.006 -0.061 0.048 
VCV2 <- impute_covariance_matrix(vi = df2$VZr, cluster = df2$cohort_ID, r = 0.5)

meta_regression6_neg <- rma.mv(yi = Zr, V = VCV2, 
                           mods = ~ k,
                           random = list(~1 | es_ID, 
                                         ~1 | paper_ID, 
                                         ~1 | cohort_ID, 
                                         ~1 | species_ID, 
                                         ~1 | phylogeny),
                           R = list(phylogeny = varcor), # added in phylogney
                           data = df2)

Model selection with negative repeatability estimates included

Here we used the MuMin package to generate all possible moderator combinations (using all five variables: annual_event, method, sex, taxa & k (number of observations per individuals)), determine the importance of the moderators, and generate model-averaged estimates when the negative repeatability estimates are included.

eval(metafor:::.MuMIn)

full_model_MuMIn_neg <- rma.mv(yi = Zr, V = VCV, 
                          mods = ~ method + 
                            taxa + 
                            sex + 
                            annual_event +
                            k, 
                          random = list(~1 | es_ID, 
                                        ~1 | paper_ID, 
                                        ~1 | cohort_ID, 
                                        ~1 | species_ID, 
                                        ~1 | phylogeny),
                          R = list(phylogeny = varcor), 
                          method = "ML", # maximum likelihood for model selection
                          data = df)

# vif.rma(full_model_MuMIn)  # No major problems of collinearity (VIF <4)

candidate_models_neg <-dredge(full_model_MuMIn_neg) # Generate all possible combinations of moderators

candidates_aic2_neg <- subset(candidate_models_neg, delta<=2) # Display all models within 2 values of AICc

importance_neg <- sw(model.avg(candidate_models_neg, subset=delta<=2))# relative importance (sum of weights) of the moderators

mod.avg_neg <- summary(model.avg(candidate_models_neg, subset=delta<=2)) # Generate model-averaged estimates 

confidence_neg <- confint(mod.avg_neg, full=TRUE) # Generate confidence intervals for the estimates averaged using full-averages procedures

Table S18

The top six models (when negative repeatability values are included) within the \(\Delta\)AIC difference of 2, and which five variables: annual_event, method, taxa, sex, & k were included (indicated by \(+\)); model weights and the sum of weights for each of the variables are included.

# creating a table
tibble(`Model (variable weight)` = c("Model1", "Model2", "Model3", "Model4", "Model5",
    "Model6", "(Sum of weights)"), annual_event = c(if_else(candidates_aic2_neg$annual_event ==
    "+", "$+$", "NA"), round(importance_neg[1], 3)), sex = c(if_else(candidates_aic2_neg$sex ==
    "+", "$+$", "NA"), round(importance_neg[2], 3)), taxa = c(if_else(candidates_aic2_neg$taxa ==
    "+", "$+$", "NA"), round(importance_neg[3], 3)), method = c(if_else(candidates_aic2_neg$method ==
    "+", "$+$", "NA"), round(importance_neg[4], 3)), k = c(if_else(candidates_aic2_neg$k <=
    0, "$+$", "NA"), round(importance_neg[5], 3)), delta_AICc = c(candidates_aic2_neg$delta,
    NA), Weight = c(candidates_aic2_neg$weight, NA)) %>%
    kable("html", digits = 3) %>%
    kable_styling("striped", position = "left")
Model (variable weight) annual_event sex taxa method k delta_AICc Weight
Model1 \(+\) NA NA NA NA 0.000 0.241
Model2 \(+\) NA \(+\) NA NA 0.428 0.195
Model3 \(+\) NA NA \(+\) NA 0.434 0.194
Model4 \(+\) \(+\) NA NA NA 0.515 0.186
Model5 \(+\) \(+\) NA NA \(+\) 1.921 0.092
Model6 \(+\) NA NA NA \(+\) 1.955 0.091
(Sum of weights) 1 0.279 0.195 0.194 0.183 NA NA

Model averaging

Table S19

The average estimates for regression coefficients (Estimate) and 95% confidence intervals (CIs) from the model averaging procedure when negative repeatability values are included using full-averages (assuming zero values for moderators when they do not occur).

# creating a table
tibble(`Fixed effect` = c("Intercept", "Depart_breed", "Nonbreed_arrival", "Nonbreed_depart",
    "Seabird", "Waterbird", "GLS", "Satellite", "k", "Female", "Male"), Estimate = mod.avg_neg$coefmat.full[,
    1], `Lower CI [0.025]` = confidence_neg[, 1], `Upper CI  [0.975]` = confidence_neg[,
    2]) %>%
    kable("html", digits = 3) %>%
    kable_styling("striped", position = "left")
Fixed effect Estimate Lower CI [0.025] Upper CI [0.975]
Intercept 0.486 0.260 0.713
Depart_breed -0.134 -0.284 0.017
Nonbreed_arrival 0.098 -0.079 0.276
Nonbreed_depart 0.222 0.055 0.389
Seabird 0.047 -0.167 0.262
Waterbird 0.017 -0.104 0.138
GLS 0.036 -0.133 0.206
Satellite 0.040 -0.156 0.236
k -0.039 -0.208 0.131
Female -0.060 -0.279 0.158
Male -0.004 -0.030 0.023

Publication Bias Analysis

We followed a recently proposed method by Nakagawa, Lagisz, Jennions, et al. (2021), which involved conducting 3 publication bias models: 1) Meta-regression with SE (uni-moderator), 2) meta-regression with year of publication (uni-moderator), and 3) all-in publication bias test (multi-moderator). We ran these publication bias models using the dataset that had negative repeatability values set to zero.

Meta-regression with SE (uni-moderator)

To test for publication bias, we first fit a phylogenetic multilevel meta-regression to explore whether there is some evidence of small-study effects in our meta-analytic dataset. To do so, we fit a uni-moderator phylogenetic multilevel meta-regression including the effect sizes’ standard errors (sei) as the only moderator.

# creating a variable for the standard error of each effect size (i.e. the square root of the sampling variance)
df$sei <- sqrt(df$VZr)

# Application of Equation 21 from the main text in Nakagawa et al. 2021
publication.bias.model.r.se <- rma.mv(yi = Zr2, V = VCV,
                                      mod = ~1 + sei,
                                      random = list(~1 | es_ID, 
                                                    ~1 | paper_ID, 
                                                    ~1 | cohort_ID, 
                                                    ~1 | species_ID, 
                                                    ~1 | phylogeny),
                                      R = list(phylogeny = varcor), # added in phylogney
                                      data=df)

# print(publication.bias.model.r.se,digits=3)

Table S20

Regression coefficients (Estimate) and 95% confidence intervals (CIs) from the univariate meta-regression fitted with sei.

# creating a table
t_sei <- tibble(`Fixed effect` = c("Intercept", "sei"), Estimate = c(publication.bias.model.r.se$b),
    `Lower CI [0.025]` = c(publication.bias.model.r.se$ci.lb), `Upper CI  [0.975]` = c(publication.bias.model.r.se$ci.ub))
# `R2` = c(orchaRd::r2_ml(publication.bias.model.r.se)[[1]], rep(NA,1)))

t_sei %>%
    kable("html", digits = 3) %>%
    kable_styling("striped", position = "left")
Fixed effect Estimate Lower CI [0.025] Upper CI [0.975]
Intercept 0.482 0.303 0.662
sei 0.213 -0.326 0.752

According to this uni-moderator meta-regression, there is no evidence of small-study effects since the slope of the moderator ‘sei’ is not statistically significant (slope = 0.213, 95% CI = [-0.326, 0.752]; R2marginal = 2.1%), showing that effect sizes with larger SE (more uncertain effect sizes) do not tend to be larger. But we will confirm this after accounting for some of the heterogeneity present in the data using the all-in publication bias test (multi-moderator meta-regression).

Meta-regression with year of publication (uni-moderator)

To test for time-lag bias (also called decline effects) we can first fit a uni-moderator phylogenetic multilevel meta-regression including the year of publication (mean-centred) as the only moderator.

df$pub_year.c <- as.vector(scale(df$pub_year, scale = F))

# Application of Equation 23 from the main text in Nakagawa et al. 2021
publication.bias.model.r.timelag <- rma.mv(yi = Zr2, V = VCV,
                                           mods= ~1 + pub_year.c,
                                           random = list(~1 | es_ID, 
                                                         ~1 | paper_ID, 
                                                         ~1 | cohort_ID, 
                                                         ~1 | species_ID, 
                                                         ~1 | phylogeny),
                                           R = list(phylogeny = varcor), # added in phylogney
                                           data=df)

Table S21

Regression coefficients (Estimate) and 95% confidence intervals (CIs) from the univariate meta-regression fitted with publication year.

# creating a table
t_pub_year <- tibble(`Fixed effect` = c("Intercept", "pub_year.c"), Estimate = c(publication.bias.model.r.timelag$b),
    `Lower CI [0.025]` = c(publication.bias.model.r.timelag$ci.lb), `Upper CI  [0.975]` = c(publication.bias.model.r.timelag$ci.ub))
# `R2` = c(orchaRd::r2_ml(publication.bias.model.r.timelag)[[1]], rep(NA,1)))

t_pub_year %>%
    kable("html", digits = 3) %>%
    kable_styling("striped", position = "left")
Fixed effect Estimate Lower CI [0.025] Upper CI [0.975]
Intercept 0.550 0.428 0.673
pub_year.c 0.008 -0.002 0.019

According to this uni-moderator meta-regression, there is no decline effects since the slope of the moderator ‘year of publication’ is essentially zero (slope = 0.008, 95% CI = [-0.002, 0.019]; R2marginal = 2.5%), showing that effect sizes have not changed linearly over time since the first effect size was published. But again, we need to confirm this pattern after accounting for some of the heterogeneity present in the data using the all-in publication bias test.

All-in publication bias test (multi-moderator)

When heterogeneity exists (which is normally the case in ecology and evolution; Senior et al. 2016), it is best to combine the above two models with other moderators since those additional moderators will generally be expected to explain some of the heterogeneity. That is, this all-in publication bias test (multi-moderator meta-regression) would be the best test of small-study (publication bias) and decline effects (time-lag bias) in most meta-analytic datasets (see Nakagawa, Lagisz, Jennions, et al., 2021). For our data, we will run a multi-moderator phylogenetic multilevel meta-regression including the effect sizes’ standard errors, the year of publication (mean-centred) and the 5 moderators included in previous models.

publication.bias.model.r.all.se <- rma.mv(yi = Zr2, V = VCV,
                                         mods= ~1 + # -1 removes the intercept
                                           sei +
                                           pub_year.c +
                                         annual_event + method + taxa + sex + k, 
                                         random = list(~1 | es_ID, 
                                                       ~1 | paper_ID, 
                                                       ~1 | cohort_ID, 
                                                       ~1 | species_ID, 
                                                       ~1 | phylogeny),
                                         R = list(phylogeny = varcor), # added in phylogney
                                         data=df)

Table S22

Regression coefficients (Estimate) and 95% confidence intervals (CIs) from the multivariate meta-regression with sei, publication year, and the five moderator variables. R2marginal = 20.8%.

# creating a table
t_all_in <- tibble(`Fixed effect` = c("Intercept", "sei", "pub_year.c", "Depart_breed",
    "Nonbreed_arrival", "Nonbreed_depart", "GLS", "Satellite", "Seabird", "Waterbird",
    "Female", "Male", "k"), Estimate = c(publication.bias.model.r.all.se$b), `Lower CI [0.025]` = c(publication.bias.model.r.all.se$ci.lb),
    `Upper CI  [0.975]` = c(publication.bias.model.r.all.se$ci.ub))
# `R2` = c(r2_publication.bias.model.r.all.se[1], rep(NA,12)))

t_all_in %>%
    kable("html", digits = 3) %>%
    kable_styling("striped", position = "left")
Fixed effect Estimate Lower CI [0.025] Upper CI [0.975]
Intercept 0.435 0.136 0.734
sei 0.176 -0.459 0.811
pub_year.c 0.009 -0.004 0.023
Depart_breed -0.113 -0.266 0.040
Nonbreed_arrival 0.100 -0.082 0.282
Nonbreed_depart 0.237 0.067 0.408
GLS -0.012 -0.299 0.275
Satellite -0.023 -0.369 0.323
Seabird 0.191 -0.066 0.448
Waterbird 0.108 -0.137 0.352
Female -0.072 -0.307 0.163
Male -0.094 -0.325 0.137
k -0.006 -0.061 0.050

The all-in publication bias test agrees with what we observed in the uni-moderator meta-regressions above. First, the multi-moderator meta-regression shows no significant slope for the moderator ‘sei’ (slope = 0.176, 95% CI = [-0.459,0.811]; Fig. S5), showing no evidence of small-study effects. In other words, the largest effect sizes in the dataset do not tend to be those with the lowest precision (i.e. larger uncertainty). Second, the all-in publication bias test also confirms that there is no evidence of decline effects in the data since the slope of the moderator ‘year of publication’ was again indistinguishable from zero (slope = 0.009, 95% CI = [-0.004,0.023]; Fig. S6).

Figure S5

predict.publication.bias.model.r.all.se.plot.1 <- predict(publication.bias.model.r.all.se,
    newmods = cbind(seq(min(df$sei), max(df$sei), 0.005), c(0), 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0))

newdat <- data.frame(sei = seq(min(df$sei), max(df$sei), 0.005), fit = predict.publication.bias.model.r.all.se.plot.1$pred,
    upper = predict.publication.bias.model.r.all.se.plot.1$ci.ub, lower = predict.publication.bias.model.r.all.se.plot.1$ci.lb,
    stringsAsFactors = FALSE)

ggplot(data = df, aes(x = sei, y = Zr2)) + geom_point(shape = 21, fill = "grey85",
    colour = "grey60", size = 3, alpha = 0.5) + geom_hline(yintercept = 0, linetype = 2,
    colour = "black", alpha = 0.5) + geom_line(data = newdat, aes(x = sei, y = fit),
    size = 1.5, colour = "darkorchid4") + geom_ribbon(data = newdat, aes(ymin = lower,
    ymax = upper, y = 0), alpha = 0.3, fill = "darkorchid4") + labs(x = "Standard error (sei)",
    y = "Effect size (Zr)") + scale_y_continuous(limits = c(-1, 2.5), breaks = seq(-1,
    2.2, by = 1)) + theme_bw() + theme(text = element_text(size = 9, colour = "black",
    hjust = 0.5), panel.grid = element_blank())

Figure S5. A bubble plot showing that effect sizes with larger standard errors do not tend to be larger, providing no evidence of small-study effects in the meta-analytic dataset. The solid line represents the model estimate and the shading shows its 95% confidence intervals.

Figure S6

predict.publication.bias.model.r.all.se.plot.2 <- predict(publication.bias.model.r.all.se,
    newmods = cbind(mean(df$sei), seq(min(df$pub_year.c), max(df$pub_year.c), 0.25),
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0))


newdat2 <- data.frame(pub_year.c = seq(min(df$pub_year.c), max(df$pub_year.c), 0.25),
    fit = predict.publication.bias.model.r.all.se.plot.2$pred, upper = predict.publication.bias.model.r.all.se.plot.2$ci.ub,
    lower = predict.publication.bias.model.r.all.se.plot.2$ci.lb, stringsAsFactors = FALSE)

ggplot(data = df, aes(x = pub_year.c, y = Zr2)) + geom_point(aes(size = (1/sqrt(VZr))),
    shape = 21, fill = "grey85", colour = "grey60", alpha = 0.5) + geom_hline(yintercept = 0,
    linetype = 2, colour = "black", alpha = 0.5) + geom_line(data = newdat2, aes(x = pub_year.c,
    y = fit), size = 1.5, colour = "darkorchid4") + geom_ribbon(data = newdat2, aes(ymin = lower,
    ymax = upper, y = 0), alpha = 0.3, fill = "darkorchid4") + labs(x = "Year of publication",
    y = "Effect size (Zr)", size = "Precison (1/SE)") + scale_y_continuous(limits = c(-1,
    2.5), breaks = seq(-1, 2.2, by = 1)) + scale_x_continuous(breaks = c(-24.0862069,
    -14.0862069, -4.0862069, 5.9137931), label = c(1990, 2000, 2010, 2020)) + theme_bw() +
    theme(text = element_text(size = 9, colour = "black", hjust = 0.5), panel.grid = element_blank(),
        legend.text = element_text(size = 8), legend.position = c(0, 0), legend.justification = c(0,
            0), legend.background = element_blank(), legend.direction = "horizontal",
        legend.title = element_text(size = 8))

Figure S6. A bubble plot showing that the overall effect size has not changed over time, where the solid line represents the model estimate and the shading shows its 95% confidence intervals, with individual data points scaled by precision (1/SE).

Acknowledgements

Many coding materials have been borrowed from these papers (Hayward et al., 2021; Pottier et al., 2021).

R Session Information

R version 3.6.2 (2019-12-12)

Platform: x86_64-w64-mingw32/x64 (64-bit)

locale: LC_COLLATE=English_United Kingdom.1252, LC_CTYPE=English_United Kingdom.1252, LC_MONETARY=English_United Kingdom.1252, LC_NUMERIC=C and LC_TIME=English_United Kingdom.1252

attached base packages: stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: pander(v.0.6.4), png(v.0.1-7), patchwork(v.1.1.1), kableExtra(v.1.3.4), MuMIn(v.1.43.17), orchaRd(v.0.0.0.9000), clubSandwich(v.0.5.3), here(v.1.0.1), cowplot(v.1.1.1), ape(v.5.5), rotl(v.3.0.11), metafor(v.3.0-2), Matrix(v.1.2-18), forcats(v.0.5.1), stringr(v.1.4.0), dplyr(v.1.0.7), purrr(v.0.3.2), readr(v.2.0.0), tidyr(v.1.1.3), tibble(v.3.1.2), ggplot2(v.3.3.5), tidyverse(v.1.3.1) and SciViews(v.0.9-13.1)

loaded via a namespace (and not attached): nlme(v.3.1-142), fs(v.1.5.0), lubridate(v.1.7.10), webshot(v.0.5.2), progress(v.1.2.2), httr(v.1.4.2), rprojroot(v.2.0.2), tools(v.3.6.2), backports(v.1.2.1), bslib(v.0.2.5.1), utf8(v.1.2.1), R6(v.2.5.0), vipor(v.0.4.5), DBI(v.1.1.1), colorspace(v.2.0-2), withr(v.2.4.2), tidyselect(v.1.1.1), prettyunits(v.1.1.1), compiler(v.3.6.2), cli(v.3.0.0), rvest(v.1.0.0), formatR(v.1.11), xml2(v.1.3.2), sandwich(v.3.0-1), labeling(v.0.4.2), sass(v.0.4.0), scales(v.1.1.1), systemfonts(v.1.0.2), digest(v.0.6.27), rmarkdown(v.2.9), svglite(v.2.0.0), rentrez(v.1.2.3), pkgconfig(v.2.0.3), htmltools(v.0.5.1.1), highr(v.0.9), dbplyr(v.2.1.1), rlang(v.0.4.11), readxl(v.1.3.1), rstudioapi(v.0.13), farver(v.2.1.0), jquerylib(v.0.1.4), generics(v.0.1.0), zoo(v.1.8-9), jsonlite(v.1.7.2), magrittr(v.2.0.1), ggbeeswarm(v.0.6.0), Rcpp(v.1.0.7), munsell(v.0.5.0), fansi(v.0.5.0), lifecycle(v.1.0.0), stringi(v.1.7.3), yaml(v.2.2.1), mathjaxr(v.1.4-0), grid(v.3.6.2), parallel(v.3.6.2), crayon(v.1.4.1), rncl(v.0.8.4), lattice(v.0.20-40), haven(v.2.4.1), hms(v.1.1.0), knitr(v.1.33), pillar(v.1.6.4), codetools(v.0.2-18), stats4(v.3.6.2), reprex(v.2.0.0), XML(v.3.99-0.3), glue(v.1.4.2), evaluate(v.0.14), modelr(v.0.1.8), vctrs(v.0.3.8), tzdb(v.0.1.1), cellranger(v.1.1.0), gtable(v.0.3.0), assertthat(v.0.2.1), xfun(v.0.24), broom(v.0.7.8), viridisLite(v.0.4.0), beeswarm(v.0.4.0), ellipse(v.0.4.2) and ellipsis(v.0.3.2)

References

Chamberlain, S. A., Dibble, C. J., Rasmussen, N. L., Allen, V., Maitner, B. S., Ahern, J. R., Roy, C. L., Meza-Lopez, M., Carrillo, J., Siemann, E., Lajeunesse, M. J., & Whitney, K. D. (2012). Does phylogeny matter? Assessing the impact of phylogenetic information in ecological meta‐analysis. 10.

Geen, G. R., Robinson, R. A., & Baillie, S. R. (2019). Effects of tracking devices on individual birds - a review of the evidence. Journal of Avian Biology, 50(2). https://doi.org/10.1111/jav.01823

Hackett, S. J., Kimball, R. T., Reddy, S., Bowie, R. C. K., Braun, E. L., Braun, M. J., Chojnowski, J. L., Cox, W. A., Han, K.-L., Harshman, J., Huddleston, C. J., Marks, B. D., Miglia, K. J., Moore, W. S., Sheldon, F. H., Steadman, D. W., Witt, C. C., & Yuri, T. (2008). A Phylogenomic Study of Birds Reveals Their Evolutionary History. Science, 320(5884), 1763–1768. https://doi.org/10.1126/science.1157704

Hayward, A., Poulin, R., & Nakagawa, S. (2021). A broadscale analysis of host‐symbiont cophylogeny reveals the drivers of phylogenetic congruence. Ecology Letters, 24(8), 1681–1696. https://doi.org/10.1111/ele.13757

Holtmann, B., Lagisz, M., & Nakagawa, S. (2017). Metabolic rates, and not hormone levels, are a likely mediator of between‐individual differences in behaviour: A meta‐analysis. Functional Ecology, 31(3), 685–696. https://doi.org/10.1111/1365-2435.12779

Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K., & Mooers, A. O. (2012). The global diversity of birds in space and time. Nature, 491(7424), 444–448. https://doi.org/10.1038/nature11631

Nakagawa, S., Lagisz, M., Jennions, M. D., Koricheva, J., Noble, D. W. A., Parker, T. H., Sánchez‐Tójar, A., Yang, Y., & O’Dea, R. E. (2021). Methods for testing publication bias in ecological and evolutionary meta‐analyses. Methods in Ecology and Evolution, 2041–210X.13724. https://doi.org/10.1111/2041-210X.13724

Nakagawa, S., Lagisz, M., O’Dea, R. E., Rutkowska, J., Yang, Y., Noble, D. W. A., & Senior, A. M. (2021). The orchard plot: Cultivating a forest plot for use in ecology, evolution, and beyond. Research Synthesis Methods, 12(1), 4–12. https://doi.org/10.1002/jrsm.1424

Nakagawa, S., & Schielzeth, H. (2010). Repeatability for Gaussian and non-Gaussian data: A practical guide for biologists. Biological Reviews, no–no. https://doi.org/10.1111/j.1469-185X.2010.00141.x

Noble, D. W. A., Lagisz, M., O’dea, R. E., & Nakagawa, S. (2017). Nonindependence and sensitivity analyses in ecological and evolutionary meta‐analyses. Molecular Ecology, 26(9), 2410–2425. https://doi.org/10.1111/mec.14031

O’Dea, R. E., Lagisz, M., Jennions, M. D., Koricheva, J., Noble, D. W. A., Parker, T. H., Gurevitch, J., Page, M. J., Stewart, G., Moher, D., & Nakagawa, S. (2021). Preferred reporting items for systematic reviews and meta‐analyses in ecology and evolutionary biology: A PRISMA extension. Biological Reviews, 96(5), 1695–1722. https://doi.org/10.1111/brv.12721

Paradis, E., & Schliep, K. (2019). Ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics, 35(3), 526–528. https://doi.org/10.1093/bioinformatics/bty633

Pottier, P., Burke, S., Drobniak, S. M., Lagisz, M., & Nakagawa, S. (2021). Sexual (in)equality? A meta‐analysis of sex differences in thermal acclimation capacity across ectotherms. Functional Ecology, 1365–2435.13899. https://doi.org/10.1111/1365-2435.13899

Viechtbauer, W. (2010). Conducting Meta-Analyses in R with the metafor Package. Journal of Statistical Software, 36(3). https://doi.org/10.18637/jss.v036.i03

---
title: 'Individual repeatability of avian migration phenology: a systematic review
  and meta-analysis'
author: Kirsty A. Franklin*, Malcolm A. C. Nicoll, Simon J. Butler, Ken Norris, Norman Ratcliffe,
  Shinichi Nakagawa & Jennifer A. Gill
date: "`r format(Sys.time(), '%d %B %Y')`"
output:
  html_document:
    code_download: yes
    code_folding: hide
    depth: 4
    number_sections: no
    theme: cosmo
    toc: yes
    toc_depth: 4
    toc_float: yes
  pdf_document:
    toc: yes
  word_document:
    toc: yes
    toc_depth: '4'
link-citations: yes
csl: journal-of-animal-ecology.csl
subtitle: Appendix S1
bibliography: Meta-analysis_supp_references.bib
always_allow_html: true
---

```{r setup, include = FALSE}
#kniter setting
knitr::opts_chunk$set(
message = FALSE,
warning = FALSE, # no warnings
cache = TRUE,# cacheing to save time when kniting
tidy = TRUE
#fig.width = 9
)

# cleaning up
 rm(list=ls())
```

## Setups

### Loading packages and custom functions

```{r load-packages}
# loading packages
#devtools::install_github("itchyshin/orchard_plot", subdir = "orchaRd", force = TRUE, build_vignettes = TRUE)
pacman::p_load(SciViews,
               tidyverse,
               dplyr,
               metafor, # package for meta-analysis
               rotl, # package for phylogeny
               ape, # package for phylogeny
               cowplot, # combining multiple plots
               here, # making folder path usable for all
               clubSandwich, # package to assist metafor
               orchaRd, # plotting orchaRd plots
               MuMIn, # multi-model inference
               kableExtra, # making nice tables
               patchwork, # putting ggplots together
               png, # reading png files
               grid, # graphic layout manipulation
               pander, # nice tables
               ggplot2 # making figures
)
```

#### Custom functions

We have 4 custom functions named : `Zr_transformation()`, `Calc_SV()`, `Zr_to_ICC()`, and `cont_gen()`, all of which are used later (see below for their functionality) and the code is included here. 

```{r custom-functions}
# Custom functions

# Getting Zr and its sampling variance from repeatability values and sample size information
# Function for Fisher's Z transformation (Zr) for correlation-based repeatabilities (r) and ICC (Holtmann et al. 2017, Table 1)
Zr_transformation <- function(r,K,Est) {
  
  if(Est == "ICC") {Zr <- 0.5*ln(((1+(K-1)*r)/(1-r)))}
  if(Est == "r") {Zr <- 0.5*ln(((1+r)/(1-r)))}
  Zr
}

# Function for sampling variance for correlation-based repeatabilities (r) and ICC (Holtmann et al. 2017, Table 1)
Calc_SV <- function(K,N,Est){
  
  if(Est == "ICC") {VZr <- K/(2*((N-2)*(K-1)))}
  if(Est == "r") {VZr <- 1/(N-3)}
  VZr
}

# Function for back-transforming Zr to ICC
Zr_to_ICC <- function(x,k){ 
  (exp(2*x)-1)/(exp(2*x)+k-1)
}

# Contrast name generator for tibble tables (from Hayward et al. 2021)
cont_gen <- function (name) {
  combination <- combn(name,2)
  name_dat <- t(combination)
  names <- paste(name_dat[ ,1], name_dat[, 2], sep = "-")
  return(names)
}

```

## Supplementary Methods

### Supplementary information for the literature search

We aimed to conduct a comprehensive search for studies estimating repeatability of temporal parameters of avian migration using a combination of approaches. We focused on arrival at, and departure from, breeding and non-breeding grounds. First, we performed a systematic search for published studies using the Web of Science and Scopus online databases on 1st June 2021 (Timespan: all years). Second, we consulted a recently published meta-analysis of hormonal, metabolic and behavioural repeatability in birds [@holtmann_metabolic_2017], which included repeatability estimates of migration. We manually checked each entry from those sources to confirm suitability for our purposes and extracted additional moderator variables to be used in our analyses (see below). Finally, in order to add to – and validate the accuracy of – the results of the literature search, we searched the reference lists of papers already in our accepted reference library. The details of these search strategies and the Boolean search strings used are presented below, along with a flow diagram (often referred to as a PRISMA flow chart – the Preferred Reporting Items in Systematic Reviews and Meta-Analyses; Figure S1) which shows the stages at which studies were disqualified or eventually used in the current study.
  
  **Web of Science Core Collection:**
  
  (TS=(“repeat\*” OR "intraclass correlation" OR “ICC” OR "individual variation" OR "intra-individual variation" OR "between-individual variation" OR "consisten\*" OR "flexib\*") AND TS=(“migration” OR “migratory”) AND TS=(“\*bird\*” OR “aves” OR “avian”)) AND (SU=(Behavioral Sciences OR Biodiversity & Conservation OR Environmental Sciences & Ecology OR Evolutionary Biology OR Genetics & Heredity OR Marine & Freshwater Biology OR Oceanography OR Veterinary Sciences OR Zoology))
  
  **Scopus:** 
  
  TITLE-ABS-KEY ( "repeat\*" OR "intraclass correlation" OR "ICC" OR "individual variation" OR "intra-individual variation" OR "between-individual variation" OR "consisten\*" OR "flexib\*" ) AND TITLE-ABS-KEY ( "migration" OR "migratory" ) AND TITLE-ABS-KEY ( "\*bird\*" OR "aves" OR "avian" ) AND ( LIMIT-TO ( SUBJAREA , "AGRI" ) OR LIMIT-TO ( SUBJAREA , "ENVI" ) OR LIMIT-TO ( SUBJAREA , "VETE" ) )


We followed reporting guidelines outlined in the PRISMA-EcoEvo checklist [@odea_preferred_2021].

### PRISMA flow chart

#### Figure S1
![](C:/Users/rst18vzu/OneDrive - University of East Anglia/PhD/Lit. review/Meta-analysis/Avian_migration_meta-analysis/figs/PRISMA_flow_chart.PNG){width=80%}

**Figure S1.** PRISMA flow chart summarising search methods and screening for studies included in analyses, and reasons for excluding studies. 

### Decision tree

#### Figure S2
![](C:/Users/rst18vzu/OneDrive - University of East Anglia/PhD/Lit. review/Meta-analysis/Avian_migration_meta-analysis/figs/Decision_tree_abstract.PNG){width=60%}

**Figure S2.** Decision tree used to evaluate studies for inclusion and exclusion at the stage of title and abstract screening.

## The Repeatability Dataset

### Table of the dataset

Below is the dataset used for our meta-analysis, followed by explanations of the variables extracted from the papers included (not all variables were used for our analyses).

#### Table S1
The meta-analytic dataset of this study.

```{r data-set}
# getting the data and formating some variables (turning chraracter vectors to factors)
df <- read_csv(here("data", "Meta-analysis_data-2022.csv"), na = "NA", show_col_types = F) %>% 
   mutate_if(is.character, as.factor)

# making a scrollable table of dataset
kable(df, "html") %>%
  kable_styling("striped", position = "left") %>%
  scroll_box(width = "100%", height = "500px")
```

A. __es_ID__: Unique ID for each row of data (i.e. each effect size).

B. __paper_ID__: Unique ID for each paper.

C. __cohort_ID__: Unique ID for each cohort of birds.

D. __species_ID__: Unique ID for each species.

E. __species_common__: Common name of species (taken from paper).

F. __species_latin__: Latin name of species (taken from paper).

G. __taxa__: Species split into three ecological groups: 'waterbird', 'seabird' or 'landbird' based on @geen_effects_2019

H. __sex__: Male, female or both/unknown.

I. __n__: Number of individuals.

J. __k__: Number of observations per individual.

K. __est__: Method of calculating repeatability: correlation coefficient (r) or intraclass correlation coefficient (ICC).

L. __R__: Repeatability value.

M. __fixed_yn__: Any fixed effects (or additional random effects) included in repeatability calculation: yes or no.

N. __fixed_var__: If yes, what additional variables are included.

O. __unstandardized_variance__: Whether unstandardized variance components are reported.

P. __method__: Tracking method: conventional (ringing, colour-ringing), geolocator (geolocation), or GPS (GPS, satellite, PTT).

Q. __annual_event__: Period of annual cycle which repeatability is measured (arrival to, or departure from, breeding or non-breeding grounds).

R. __location__: Location of tagging (as written by paper).

S. __continent__: Continent of tagging location: North America, Europe, or Other.

T. __long__: Longitude of tagging location.

U. __lat__: Latitude of tagging location.

V. __tag_period__: Whether individuals were tagged on the breeding or non-breeding grounds.

W. __notes__: general comments

X. __data_location__: Where in the paper the data is located.

Y. __data_presentation__: Text, figure, or table.

Z. __title__: Title of the paper.

AA. __pub_year__: Publication year of paper.

AB: __authors__: Authors of the paper.

AC: __journal__: Journal the paper was published in.

AD: __DOI__: DOI of the paper.

AE: __fulltext_ID__: An ID that is used to link the paper for data extraction to the record of screened full texts.

### Sample sizes

#### Table S2
Sample sizes for our data set in terms of effect sizes, cohorts, studies, species, and the number of effect sizes in the different levels of categorical variables (factors), split by ecological group (seabird, waterbird and landbird), and overall.   

```{r sample-sizes}

# making a table of sample sizes for different variables
df %>% group_by(taxa) %>% 
  summarise(
    `Effect sizes (analyses)` = n(),
    `Cohort` = n_distinct(cohort_ID),
    `Studies` = n_distinct(authors),
    `Species` = n_distinct(species_ID),
    `Arrival at breeding grounds` = sum(annual_event == "Arrival_breed", na.rm=T),
    `Departure from breeding grounds` = sum(annual_event == "Depart_breed", na.rm=T),
    `Arrival at non-breeding grounds` = sum(annual_event == "Nonbreed_arrival", na.rm=T),
    `Departure from non-breeding grounds` = sum(annual_event == "Nonbreed_depart", na.rm=T),
    `Conventional method` = sum(method == "Conventional", na.rm = T),
    `GLS method` = sum(method == "GLS", na.rm = T),
    `Satellite method`  = sum(method == "Satellite", na.rm = T),
    `Female` = sum(sex == "F", na.rm=T),
    `Male` = sum(sex == "M", na.rm=T),
    `Mixed sex` = sum(sex == "B", na.rm=T)
  ) -> n_table1

n_table2 <-t(n_table1[,-1])
colnames(n_table2) <- c("Landbird", "Seabird", "Waterbird")
n_table2 %>% as_tibble(rownames = "Number of") %>% 
  mutate(All = Landbird + Seabird + Waterbird) %>%  
  kable() %>% kable_styling("striped", position = "left") %>%
  pack_rows("All data", 1, 4) %>%
  pack_rows("Annual event", 5, 8) %>%
  pack_rows("Tracking method", 9, 11) %>%
  pack_rows("Sex", 12, 14)

```

### Marking locations of studies
#### Figure 1
```{r figure1, fig.width=10, fig.height=5}

worldmap <- map_data('world') # get map data

# Plot of world with all tracking study locations
world <- ggplot() + geom_polygon(data = worldmap, aes(x=long, y = lat, group = group), fill = "grey80", 
                                  colour = NA, size = 0.1) + 
    coord_fixed(1.3) +
    theme_minimal() +
    scale_colour_manual(values = c("Seabird" = "#009E73",  "Landbird" = "#D55E00",  "Waterbird"= "#0072B2")) +
    geom_point(data=df, aes(x= long, y =lat, colour=taxa, shape=method), size =1.5) +
    geom_rect(aes(xmin= -12, xmax= 23, ymin= 32, ymax= 64), fill = NA, colour = "black", size = 0.3) +
    theme(panel.grid = element_line(colour = "white"), axis.title = element_blank(), axis.text = element_blank(), 
          axis.ticks = element_blank(), legend.position = "none")

## Zoomed in plot on Europe
zoom <- ggplot() + geom_polygon(data = worldmap, aes(x=long, y = lat, group = group), fill = "grey80", 
                                 colour = NA, size = 0.2) + 
    coord_sf(xlim= c(-11,23), ylim= c(32,64)) +
    theme_minimal() +
    theme(legend.text=element_text(size=6), legend.title=element_text(size=7, face="bold"), legend.key.size = unit(1, "mm")) +
    theme(panel.grid = element_line(colour= "white"),
          axis.title = element_blank(), axis.text = element_blank(),
          panel.border = element_rect(colour = "black", fill = NA, size=0.5), 
          axis.ticks = element_blank()) +
    scale_colour_manual(name="Ecological group", values = c("Seabird" = "#009E73",  "Landbird" = "#D55E00",  "Waterbird"= "#0072B2")) +
    scale_shape_discrete(name = "Method") +
    geom_point(data=df, aes(x= long, y =lat, shape=method, colour=taxa),size =1.5)

figure1 <- plot_grid(world, zoom, nrow=1, rel_widths = c(1.3,1))
# ggsave("figs/Map_of_tagging_locations.pdf", dpi=600, width=180, height=60, units="mm")

figure1

```

**Figure 1.** The marking locations of birds for all studies with repeatability estimates collated from the literature and included in analyses, coloured by ecological group (waterbird, seabird, or landbird), and shaped by tracking method (conventional, satellite, or GLS).

## Meta-analysis

### Calculating effect sizes

We created our effect size (correlation coefficient *r* and intra-class correlation coefficient *ICC* and their Fisher's z transformation *Zr*) from repeatability values and associated sample sizes. We followed the equations outlined in @holtmann_metabolic_2017. We converted the negative repeatability estimates (n=13) in our dataset to zero, as they often only reflect noise around a statistical zero [@nakagawa_repeatability_2010], but you can find the meta-analytic and meta-regression models with the negative repeatability estimates included in Tables S12-19. 

```{r effect-sizes}
# Creating new columns for effect sizes (Zr) and sampling variance (VZr)
df$VZr <- df$Zr <- NA

# Calculate effect sizes using custom function
for (i in as.numeric(rownames(df))) {
  r <- df$R[i]
  K <- df$k[i]
  Est <- df$est[i]
  
  Zr <- Zr_transformation(r, K, Est)
  
  df$Zr[i] <- Zr
  
}
# Calculate sampling variances using custom function
for (i in as.numeric(rownames(df))) {
  K <- df$k[i]
  N <- df$n[i]
  Est <- df$est[i]
  
  VZr <- Calc_SV(K,N,Est)
  
  df$VZr[i] <- VZr
  
}

# Add a new column with all negative repeatability values set to zero
df$Zr2 <- ifelse(df$Zr < 0, 0, df$Zr+0)

```


### Phylogenetic tree
Since multiple bird species (n = `r (dplyr::n_distinct(df$species_ID))`) are included in this dataset, we needed to consider phylogenetic non-independence in our models [@chamberlain_does_2012, Cinar et al. 2022]. To generate the phylogeny, we used a phylogenetic tree from @jetz_global_2012 (provided by Benedikt Holtmann), which was prepared on the basis of Hackett backbone [Hackett tree; @hackett_phylogenomic_2008]. We first searched the tree for the species in our dataset to confirm and correct the species names. 

- _Limosa lapponica baueri_ was assumed to be synonym to _Limosa lapponica_
- _Limosa limosa limosa_ and _Limosa limosa islandica_ were assumed to be synonym to _Limosa limosa_
- _Cygnus columbianus bewickii_ was assumed to be synonym to _Cygnus columbianus_
- _Catharacta antarctica lonnbergi_ was assumed to be synonym to _Catharacta antarctica_
- _Pterodroma deserta_ was assumed to be synonym to _Pterodroma mollis_
- _Chen canagicus_ was assumed to be synonym to _Chen canagica_
- _Anser caerulescens atlanticus_ was assumed to be synonym to _Chen caerulescens_

We then trimmed the tree to include only the species names in our data set, and computed branch lengths using Grafen's method (Grafen, 1989) in the compute.brlen function in the R package ape [@paradis_ape_2019].

#### Figure S3
```{r phylo-tree, fig.width=10, fig.height=8}

species_list <- unique(df$species_latin) # use unique() as some names are repeated
species_list<-gsub(" ", "_", species_list) # replace spaces with underscore so they match tree

# Load bird supertree based on Hackett's backbone 
bird_tree_hackett <- read.tree(here("data", "Hackett.tre")) # tree provided by Benedikt Holtmann

# Prune phylogentic tree for meta-analysis

# Check the tree
# bird_tree_hackett # 9993 tips = species
# str(bird_tree_hackett) # has edge (branch) lengths
bird_tree_hackett <- collapse.singles(bird_tree_hackett)

# Get only bird species from the supertree that are also included in collected data
bird_tree_species <- as.character(bird_tree_hackett$tip.label)

# Check the overlap of species names between collected data file and the supertree
# All species should be present. If not, species names may not match with names in the supertree
# intersect(bird_tree_species, species_list) 
# character(39) - should be 47, need to check which species do/do not match

# gives list of species names which are not matched
# species_list[!species_list %in% bird_tree_species]
#"Limosa_lapponica_baueri"
#"Cygnus_columbianus_bewickii"
#"Limosa_limosa_limosa"
#"Limosa_limosa_islandica"
#"Catharacta_antarctica_lonnbergi"
#"Pterodroma_deserta"
#"Chen_canagicus"
#"Anser_caerulescens_atlanticus"

# making a new list so it is clear that this list of species is an updated version compared to the original one
species_list_hackett <- species_list

# Changing subspecies to species, or old genus names from papers to new 
# Only Pterodroma deserta: 3 species in the Pterodroma feae/madeira/desertae complex were once believed to be subspecies of a single species: Pterodroma mollis
species_list_hackett <- replace(species_list_hackett, species_list_hackett=="Limosa_lapponica_baueri", "Limosa_lapponica")
species_list_hackett <- replace(species_list_hackett, species_list_hackett=="Limosa_limosa_limosa" | species_list_hackett=="Limosa_limosa_islandica" , "Limosa_limosa")
species_list_hackett <- replace(species_list_hackett, species_list_hackett=="Cygnus_columbianus_bewickii", "Cygnus_columbianus")
species_list_hackett <- replace(species_list_hackett, species_list_hackett=="Catharacta_antarctica_lonnbergi", "Catharacta_antarctica")
species_list_hackett <- replace(species_list_hackett, species_list_hackett=="Pterodroma_deserta", "Pterodroma_mollis")
species_list_hackett <- replace(species_list_hackett, species_list_hackett=="Chen_canagicus", "Chen_canagica")
species_list_hackett <- replace(species_list_hackett, species_list_hackett=="Anser_caerulescens_atlanticus", "Chen_caerulescens")

# Now check and see if all species are present in supertree
# intersect(bird_tree_species, species_list_hackett) # = 47 (not 48, because L. l. limosa and L. i. islandica both changed to L. limosa)

# Prune supertree to the list of taxa included in the data
pruned_birds_stree <- drop.tip(bird_tree_hackett, bird_tree_hackett$tip.label[-match(species_list_hackett, bird_tree_hackett$tip.label)])

# Check if tree is binary and ultrametric
# is.binary(pruned_birds_stree) # TRUE
# is.ultrametric(pruned_birds_stree) # TRUE

# Save pruned tree to be use in the meta-analysis
write.tree(pruned_birds_stree,
           file = here("data", "birds_meta-analysis_tree.tre"), append = FALSE,
           digits = 10, tree.names = FALSE
)

# Make phylogenetic correlation matrix

# Using metafor - correlation matrix for species in tree
varcor <- vcv(pruned_birds_stree, corr = TRUE)

# Create a column called phylogeny, which matches the tree
# Copied column to edit species names to match tree (as some sp. names are from old papers, or are subspecies)
df$species_latin_hackett <- as.character(df$species_latin) 
df$species_latin_hackett <-replace(df$species_latin_hackett, df$species_latin_hackett == "Limosa lapponica baueri", "Limosa lapponica")
df$species_latin_hackett <- replace(df$species_latin_hackett, df$species_latin_hackett=="Limosa limosa limosa" | df$species_latin_hackett=="Limosa limosa islandica" , "Limosa limosa")
df$species_latin_hackett <-replace(df$species_latin_hackett, df$species_latin_hackett == "Cygnus columbianus bewickii", "Cygnus columbianus")
df$species_latin_hackett <-replace(df$species_latin_hackett, df$species_latin_hackett == "Catharacta antarctica lonnbergi", "Catharacta antarctica")
df$species_latin_hackett <-replace(df$species_latin_hackett, df$species_latin_hackett == "Pterodroma deserta", "Pterodroma mollis")
df$species_latin_hackett <-replace(df$species_latin_hackett, df$species_latin_hackett == "Chen canagicus", "Chen canagica")
df$species_latin_hackett <-replace(df$species_latin_hackett, df$species_latin_hackett == "Anser caerulescens atlanticus", "Chen caerulescens")

df$phylogeny<-gsub(" ", "_", df$species_latin_hackett)

# Plot tree to see how it looks like
plot(pruned_birds_stree, label.offset = 2, cex = 0.8, main = "'Hackett tree'", cex.main = 1, line = 0.5) # with branch lengths
```

**Figure S3.** Phylogenetic tree (with Hackett backbone) used for phylogenetic meta-analysis and meta-regression on repeatability in avian migratory timings.

### Meta-analytic model

#### Running multilevel meta-analytic model with and without phylogeny

We used the `rma.mv` function from the package `metafor` [@viechtbauer_conducting_2010] to run all meta-analytic models and meta-regressions. This function allows us to incorporate variance-covariance matrices in the V term. We assumed a correlation of 0.5 
[@noble_nonindependence_2017] that will be fitted as part of the random effect structure of our models but also test different levels of correlation (see Sensitivity analysis).

As we have a multi-species dataset, we use the model that accounts for both the non-phylogenetic and phylogenetic species-level variance in addition to the full multilevel structure of the data as any attempts to simplify this model, such as using only the phylogenetic variance component, may lead to erroneous inferences from the data (Cinar et al. 2022).

```{r meta-analytic-models}
# Create a variance-covariance matrix at the cohort level
VCV <- impute_covariance_matrix(vi = df$VZr, cluster = df$cohort_ID, r = 0.5)
# Why 0.5 - see https://onlinelibrary.wiley.com/doi/full/10.1111/mec.14031

ma_model1 <- rma.mv(yi = Zr2, V = VCV,
                   random = list(~1 | es_ID, 
                                 ~1 | paper_ID, 
                                 ~1 | cohort_ID, 
                                 ~1 | species_ID), 
                   data = df)

ma_model2 <- rma.mv(yi = Zr2, V = VCV, 
                      random = list(~1 | es_ID, 
                                    ~1 | paper_ID, 
                                    ~1 | cohort_ID, 
                                    ~1 | species_ID, # non-phylo effect
                                    ~1 | phylogeny), # phylo effect
                      R = list(phylogeny = varcor), # phylogenetic relatedness
                      data = df)

```

#### Table S3
Overall effects (meta-analytic means) and 95% confidence intervals (CIs) both in *Zr* and back-transformed to *ICC*, and heterogeneity, *I*^2^, for the multilevel intercept-only meta-analysis models including and excluding phylogeny.

```{r meta-analytic-table}
## # estimating I2 as measure of heterogeneity
i2_ma1 <- round(i2_ml(ma_model1)*100,1)
i2_ma2 <- round(i2_ml(ma_model2)*100,1)

# Back-transform to ICC
ma1 <- mod_results(ma_model1, mod="Int")
ma1_mod_table <- ma1$mod_table

ma2 <- mod_results(ma_model2, mod="Int")
ma2_mod_table <- ma2$mod_table

# need to calculate k for whole data set to use in formula

k_all <- mean(df$k)

for(i in names(ma1_mod_table)[2:6]){
  
  ma1_mod_table[i] <- Zr_to_ICC(ma1_mod_table[i], k_all)
  
}

for(i in names(ma2_mod_table)[2:6]){
  
  ma2_mod_table[i] <- Zr_to_ICC(ma2_mod_table[i], k_all)
  
}

# creating a table
tibble(
  Model = c("Meta-analysis (Zr)", "Meta-analysis (ICC)", "Meta-analysis phylo (Zr)", "Meta-analysis phylo (ICC)"),
  `Overall mean` = c(ma_model1$b, ma1_mod_table$estimate, ma_model2$b, ma2_mod_table$estimate),
  `Lower CI [0.025]` = c(ma_model1$ci.lb, ma1_mod_table$lowerCL, ma_model2$ci.lb, ma2_mod_table$lowerCL),
  `Upper CI [0.975]` = c(ma_model1$ci.ub, ma1_mod_table$upperCL, ma_model2$ci.ub, ma2_mod_table$upperCL),
  `I^2^~total~` = c(i2_ma1[1], NA, i2_ma2[1], NA),
  `I^2^~es~` = c(i2_ma1[2], NA, i2_ma2[2], NA),
  `I^2^~paper~` = c(i2_ma1[3], NA, i2_ma2[3], NA),
  `I^2^~cohort~` = c(i2_ma1[4], NA, i2_ma2[4], NA),
  `I^2^~species~` = c(i2_ma1[5], NA, i2_ma2[5], NA),
  `I^2^~phylo~` = c(NA, NA, i2_ma2[6], NA),) %>% 
  kable("html",  digits = 3) %>%
  kable_styling("striped", position = "left")
```

#### Figure 2a

```{r figure2a,  fig.width=7, fig.height=2}

ma2_data <- ma2$data

# back-transform model results to ICC
# need to calculate k for whole data set to use in formula

ma2_data$yi_ICC <- Zr_to_ICC(ma2_data$yi, k_all)

ma2_data$moderator <- factor(ma2_data$moderator, levels = ma2_mod_table$name, labels = ma2_mod_table$name)
ma2_data$scale <- (1/sqrt(ma2_data[,"vi"]))
ma2_mod_table$K <- as.vector(by(ma2_data, ma2_data[,"moderator"], function(x) length(x[,"yi"])))
ma2_group_no <- nrow(ma2_mod_table)

# colour blind friendly colours with grey
cbpl <- c("#E69F00","#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#56B4E9", "#999999")

# creating an orchard plot
fig_ma2 <- ggplot(data = ma2_mod_table, aes(x = estimate, y = "Overall mean")) + 
     scale_x_continuous(limits = c(-0.37, 1), breaks = seq(-0.4, 1, by = 0.2), labels = c("-0.4", "-0.2", "0.0", "0.2", "0.4", "0.6", "0.8", "1.0")) +
    ggbeeswarm::geom_quasirandom(data = ma2_data, aes(x = yi_ICC, y = "Overall mean", size = scale), fill="black", groupOnX=FALSE, alpha = 0.2) +
    geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = F, size = 0.5, alpha = 0.6) +
    geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend = F, size = 1.2) + 
    geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
    geom_point(fill="black", size = 3, shape = 21) + 
    annotate('text', x = 0.93, y = (seq(1, ma2_group_no, 1)+0.35), label= paste("italic(k)==", ma2_mod_table$K), parse=TRUE, hjust = "left", size=3.5) +
    ggplot2::theme_bw() +
    ggplot2::guides(fill="none", colour="none") +
    ggplot2::theme(legend.position = c(1,0), legend.justification = c(1,0), legend.title = element_text(size=8), 
                   legend.text = element_text(size=8), legend.key = element_rect(colour = NA, fill = NA), 
                   legend.background = element_blank(), legend.direction = "horizontal",
                   axis.text.x = element_text(size=9),
                   axis.text.y = element_text(size=9, colour="black", hjust=0.5, angle=90), 
                   axis.title.y = element_blank(), legend.key.size = unit(0.5,'cm')) +
    ggplot2::labs(size=paste("Precision (1/SE)"), x=paste("Effect size (ICC)"))

# For Figure 2

a <- ggplot(data = ma2_mod_table, aes(x = estimate, y = "Overall mean")) + 
     scale_x_continuous(limits = c(-0.37, 1), breaks = seq(-0.4, 1, by = 0.2), labels = c("-0.4", "-0.2", "0.0", "0.2", "0.4", "0.6", "0.8", "1.0")) +
    ggbeeswarm::geom_quasirandom(data = ma2_data, aes(x = yi_ICC, y = "Overall mean", size = scale), fill="black", groupOnX=FALSE, alpha = 0.2) +
    geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = F, size = 0.5, alpha = 0.6) + # CI
    geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend = F, size = 1.2) + 
    geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
    geom_point(fill="black", size = 3, shape = 21) + 
    annotate('text', x = 0.93, y = (seq(1, ma2_group_no, 1)+0.35), label= paste("italic(k)==", ma2_mod_table$K), parse=TRUE, hjust = "left", size=3.5) +
    ggplot2::theme_bw() +
    ggplot2::guides(fill="none", colour="none") +
    ggplot2::theme(legend.position = c(1,0), legend.justification = c(1,0), legend.title = element_text(size=8), 
                   legend.text = element_text(size=8), legend.key = element_rect(colour = NA, fill = NA), 
                   legend.background = element_blank(), legend.direction = "horizontal", 
                   axis.text.y = element_text(size=9, colour="black", hjust=0.5, angle=90), 
                   axis.title = element_blank(), legend.key.size = unit(0.5,'cm')) +
    ggplot2::labs(size=paste("Precision (1/SE)"))

fig_ma2

```

**Figure 2a.** Repeatability of avian migration timing for all estimates together. The plot shows the phylogenetic meta-analytic mean with 95% confidence intervals (thick lines, indicating uncertainty around the overall estimate) and 95% prediction intervals (thin lines, indicating the possible range for 95% of new (or simply not included) effect sizes), observed effect sizes (back-transformed to ICC) scaled by precision (circles) and k = number of effect sizes. The graph was constructed using code adapted from the `orchard_plot` function in the orchaRd package [@nakagawa_orchard_2021].

## Meta-regression

We ran a univariate meta-regression model for each of the following moderators: 1) `annual_event`, 2) `method`, 3) `taxa`, 4) `sex` and 5) number of samples per individual (`k`).

### Univariate (uni-predictor) analyses

We first conducted a series of meta-regression models with each of the moderators introduced above. 

#### Period of the annual cycle

```{r meta-regression-annual-event}
# meta-regression: mutiple intercepts
meta_regression1 <- rma.mv(yi = Zr2, V = VCV, 
                           mods = ~ annual_event,
                           random = list(~1 | es_ID, 
                                         ~1 | paper_ID, 
                                         ~1 | cohort_ID, 
                                         ~1 | species_ID, 
                                         ~1 | phylogeny),
                           R = list(phylogeny = varcor), # added in phylogney
                           data = df)

meta_regression1b <- rma.mv(yi = Zr2, V = VCV, 
                            mods = ~ relevel(annual_event, ref = "Depart_breed"),
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor), # added in phylogney
                            data = df)


meta_regression1c <- rma.mv(yi = Zr2, V = VCV, 
                            mods = ~ relevel(annual_event, ref = "Nonbreed_depart"),
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor), # added in phylogney
                            data = df)

# meta-regression: contrast (for orchard plot)
meta_regression1d <- rma.mv(yi = Zr2, V = VCV, 
                            mods = ~ annual_event -1,
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor), # added in phylogney
                            data = df)
```

#### Table S4
Regression coefficients (Estimate) and 95% confidence intervals (CIs) both in Zr and back-transformed to ICC from the meta-regression with `annual_event`. Note that `mu` means the group mean while `beta` represents the contrast between two groups in the Unit column. *R<sup>2</sup><sub>marginal</sub>* = `r round(orchaRd::r2_ml(meta_regression1)[[1]]*100,1)`%.

```{r annual-event-table}
# getting marginal R2
r2_meta_regression1 <- r2_ml(meta_regression1)

# getting estimates
# including back-transformation to ICC

mr1 <- mod_results(meta_regression1d, mod="annual_event")
mr1_mod_table <- mr1$mod_table

mr1_data <- mr1$data

# calculate k for each method separately

# df %>% group_by(annual_event) %>% summarise(mean(k))

mr1_data <- mr1_data %>% mutate(k = case_when(moderator == "Arrival_breed" ~ 2.55, 
                                              moderator == "Depart_breed" ~ 2.45,
                                              moderator == "Nonbreed_arrival" ~ 3.20,
                                              moderator == "Nonbreed_depart" ~ 2.94))

mr1_data$yi_ICC <- Zr_to_ICC(mr1_data$yi, mr1_data$k)

mr1_mod_table$k <- c(2.55, 2.45, 3.20, 2.94)

for(i in names(mr1_mod_table)[2:6]){
  
  mr1_mod_table[i] <- Zr_to_ICC(mr1_mod_table[i], mr1_mod_table$k)
  
}

# creating a table
tibble(
  `Fixed effect` = c(rep(as.character(mr1_mod_table$name),2), cont_gen(mr1_mod_table$name)),
  `Unit` = c(rep(c("Zr (mu)", "ICC (mu)"),each = 4), rep("Zr (beta)", 6)),
  `Estimate` = c(meta_regression1d$b, mr1_mod_table$estimate, 
                 meta_regression1$b[-1], meta_regression1b$b[-(1:2)], meta_regression1c$b[-(1:3)]),
  `Lower CI [0.025]` = c(meta_regression1d$ci.lb, mr1_mod_table$lowerCL,
                         meta_regression1$ci.lb[-1], meta_regression1b$ci.lb[-(1:2)], meta_regression1c$ci.lb[-(1:3)]),
  `Upper CI  [0.975]` = c(meta_regression1d$ci.ub, mr1_mod_table$upperCL,
                         meta_regression1$ci.ub[-1], meta_regression1b$ci.ub[-(1:2)], meta_regression1c$ci.ub[-(1:3)])) -> t_annual_event
 # `R2` = c(r2_meta_regression1[1], rep(NA,13))) -> 

t_annual_event %>% kable("html", digits = 3) %>%
  kable_styling("striped", position = "left")
               
```

#### Figure 2b 

```{r figure2b, fig.width=7, fig.height=4}
# Orchard plot for annual event with ICC

mr1_data$moderator <- factor(mr1_data$moderator, levels = mr1_mod_table$name, labels = mr1_mod_table$name)
mr1_data$scale <- (1/sqrt(mr1_data[,"vi"]))
mr1_mod_table$K <- as.vector(by(mr1_data, mr1_data[,"moderator"], function(x) length(x[,"yi"])))
mr1_group_no <- nrow(mr1_mod_table)

image_ABegg <- readPNG(here("images/AB_eggs_icon.png"))
image_ANB <- readPNG(here("images/ANB_icon.png"))
image_DBchick <- readPNG(here("images/DB_chicks_icon.png"))
image_DNB <- readPNG(here("images/DNB_icon.png"))

# creating an orchard plot

# fig_annual_event <- ggplot(data = mr1_mod_table, aes(x = estimate, y = name)) + 
#     scale_x_continuous(limits = c(-0.4, 1), breaks = seq(-0.4, 1, by = 0.2), labels = c("-0.4", "-0.2", "0.0", "0.2", # "0.4", "0.6", "0.8", "1.0")) + 
#     ggbeeswarm::geom_quasirandom(data = mr1_data, aes(x = yi_ICC, y = moderator, size = scale, colour=moderator), groupOnX=FALSE, # alpha = 0.4) +
#     geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = F, size = 0.5, alpha = 0.6) + # CI
#     geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend = F, size = 1.2) + 
#     geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
#     geom_point(aes(fill=name), size = 3, shape = 21) + 
#     annotate('text', x = 0.93, y = (seq(1, mr1_group_no, 1)+0.35), label= paste("italic(k)==", mr1_mod_table$K), parse=TRUE, hjust # = "left", size=3.5) +
#     ggplot2::theme_bw() +
#     ggplot2::guides(fill="none", colour="none") +
#     ggplot2::theme(axis.text.y = element_text(size=9, colour="black", hjust=0.5, angle=90),
#                    legend.position = "none", axis.title.y = element_blank(), axis.text.x = element_text(size=9)) +
#     scale_y_discrete(labels=c("Arrival_breed" = "Arrive", "Depart_breed" = "Depart", "Nonbreed_arrival" = "Arrive", # "Nonbreed_depart" = "Depart")) +
# 	      scale_fill_manual(values=cbpl) +
# 	      scale_colour_manual(values=cbpl) +
#     ggplot2::labs(size=paste("Precision (1/SE)"), x=paste("Effect size (ICC)")) +
#     annotation_custom(grid::rasterGrob(image_DNB), xmin = -0.45, xmax = -0.25, ymin = 3.7, ymax = 4.3) +
#     annotation_custom(grid::rasterGrob(image_ANB), xmin = -0.45, xmax = -0.25, ymin = 2.7, ymax = 3.3) +
#     annotation_custom(grid::rasterGrob(image_DBchick), xmin = -0.45, xmax = -0.25, ymin = 1.7, ymax = 2.3) +
#     annotation_custom(grid::rasterGrob(image_ABegg), xmin = -0.45, xmax = -0.25, ymin = 0.7, ymax = 1.3)
# 
# # for Figure 2
# 
# b <- ggplot(data = mr1_mod_table, aes(x = estimate, y = name)) + 
#     scale_x_continuous(limits = c(-0.37, 1), breaks = seq(-0.4, 1, by = 0.2), labels = c("-0.4", "-0.2", "0.0", "0.2", "0.4", "0.6", "0.8", "1.0")) + 
#     ggbeeswarm::geom_quasirandom(data = mr1_data, aes(x = yi_ICC, y = moderator, size = scale, colour=moderator), groupOnX=FALSE, alpha = 0.4) +
#     geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = F, size = 0.5, alpha = 0.6) + # CI
#     geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend = F, size = 1.2) + 
#     geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
#     geom_point(aes(fill=name), size = 3, shape = 21) + 
#     annotate('text', x = 0.93, y = (seq(1, mr1_group_no, 1)+0.35), label= paste("italic(k)==", mr1_mod_table$K), parse=TRUE, hjust = "left", size=3.5) +
#     ggplot2::theme_bw() +
#     ggplot2::guides(fill="none", colour="none") +
#     ggplot2::theme(axis.text.y = element_text(size=9, colour="black", hjust=0.5, angle=90),
#                    legend.position = "none", axis.title = element_blank()) +
#     scale_y_discrete(labels=c("Arrival_breed" = "Arrive", "Depart_breed" = "Depart", "Nonbreed_arrival" = "Arrive", "Nonbreed_depart" = "Depart")) +
# 	      scale_fill_manual(values=cbpl) +
# 	      scale_colour_manual(values=cbpl) +
#     annotation_custom(grid::rasterGrob(image_DNB), xmin = -0.45, xmax = -0.25, ymin = 3.7, ymax = 4.3) +
#     annotation_custom(grid::rasterGrob(image_ANB), xmin = -0.45, xmax = -0.25, ymin = 2.7, ymax = 3.3) +
#     annotation_custom(grid::rasterGrob(image_DBchick), xmin = -0.45, xmax = -0.25, ymin = 1.7, ymax = 2.3) +
#     annotation_custom(grid::rasterGrob(image_ABegg), xmin = -0.45, xmax = -0.25, ymin = 0.7, ymax = 1.3)

# fig_annual_event

```

![](C:/Users/rst18vzu/OneDrive - University of East Anglia/PhD/Lit. review/Meta-analysis/Avian_migration_meta-analysis/figs/Fig2b_zero_for_Rmd.png){width=85%}

**Figure 2b.** Repeatability of avian migration timing for annual migration events. The plot shows the group-wise means with 95% confidence intervals (thick lines) and 95% prediction intervals (thin lines), observed effect sizes (back-transformed to ICC) scaled by precision (circles) and k = number of effect sizes. Note this plot was edited in Affinity Designer after construction in R.

#### The effect of tracking method

```{r meta-regression-method}
meta_regression2 <- rma.mv(yi = Zr2, V = VCV, 
                      mods = ~ method,
                      random = list(~1 | es_ID, 
                                    ~1 | paper_ID, 
                                    ~1 | cohort_ID, 
                                    ~1 | species_ID, 
                                    ~1 | phylogeny),
                      R = list(phylogeny = varcor),
                      data = df)

# reordering
# df$method <- factor(df$method, levels = c("Conventional", "GLS", "Satellite"))

meta_regression2b <- rma.mv(yi = Zr2, V = VCV, 
                            mods = ~ relevel(method, ref = "GLS"),
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor),
                            data = df)

# Orchard plot - need meta-regression without intercept
meta_regression2c <- rma.mv(yi = Zr2, V = VCV, 
                            mods = ~ method -1,
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor),
                            data = df)

```

#### Table S5
Regression coefficients (Estimate) and 95% confidence intervals (CIs) both in Zr and back-transformed to ICC from the meta-regression with `method`. Note that `mu` means the group mean while `beta` represents the contrast between two groups in the Unit column. *R<sup>2</sup><sub>marginal</sub>* = `r round(orchaRd::r2_ml(meta_regression2)[[1]]*100,1)`%.

```{r method-table}
# getting marginal R2
r2_meta_regression2 <- r2_ml(meta_regression2)

# getting estimates
# including back-transformation to ICC

mr2 <- mod_results(meta_regression2c, mod="method")
mr2_mod_table <- mr2$mod_table

mr2_data <- mr2$data

# calculate k for each method separately

#df %>% group_by(method) %>% summarise(mean(k))


mr2_data <- mr2_data %>% mutate(k = case_when(moderator == "GLS" ~ 2.20, 
                                              moderator == "Conventional" ~ 3.13,
                                              moderator == "Satellite" ~ 3.28))

mr2_data$yi_ICC <- Zr_to_ICC(mr2_data$yi, mr2_data$k)

mr2_mod_table$k <- c(3.13, 2.20, 3.28)

for(i in names(mr2_mod_table)[2:6]){
  
  mr2_mod_table[i] <- Zr_to_ICC(mr2_mod_table[i], mr2_mod_table$k)
  
}

# creating a table
tibble(
  `Fixed effect` = c(rep(as.character(mr2_mod_table$name),2), cont_gen(mr2_mod_table$name)),
  `Unit` = c(rep(c("Zr (mu)", "ICC (mu)"),each = 3), rep("Zr (beta)", 3)),
  `Estimate` = c(meta_regression2c$b, mr2_mod_table$estimate, 
                 meta_regression2$b[-1], meta_regression2b$b[-(1:2)]),
  `Lower CI [0.025]` = c(meta_regression2c$ci.lb, mr2_mod_table$lowerCL,
                         meta_regression2$ci.lb[-1], meta_regression2b$ci.lb[-(1:2)]),
  `Upper CI  [0.975]` = c(meta_regression2c$ci.ub, mr2_mod_table$upperCL,
                         meta_regression2$ci.ub[-1], meta_regression2b$ci.ub[-(1:2)])) -> t_method
 # `R2` = c(r2_meta_regression2[1], rep(NA,8))) -> t_method

t_method %>% kable("html", digits = 3) %>%
  kable_styling("striped", position = "left") 
```

#### Figure 2c
```{r figure2c, fig.width=7, fig.height= 3}

mr2_data$moderator <- factor(mr2_data$moderator, levels = mr2_mod_table$name, labels = mr2_mod_table$name)
mr2_data$scale <- (1/sqrt(mr2_data[,"vi"]))
mr2_mod_table$K <- as.vector(by(mr2_data, mr2_data[,"moderator"], function(x) length(x[,"yi"])))
mr2_group_no <- nrow(mr2_mod_table)

image_conventional <- readPNG(here("images/Conventional_icon.png"))
image_GLS <- readPNG(here("images/GLS_icon.png"))
image_satellite <- readPNG(here("images/Satellite_icon.png"))

# creating orchaRd plot
fig_method <- ggplot(data = mr2_mod_table, aes(x = estimate, y = name)) + 
    scale_x_continuous(limits = c(-0.37, 1), breaks = seq(-0.4, 1, by = 0.2), labels = c("-0.4", "-0.2", "0.0", "0.2", "0.4", "0.6", "0.8", "1.0")) + 
    ggbeeswarm::geom_quasirandom(data = mr2_data, aes(x = yi_ICC, y = moderator, size = scale, colour=moderator), groupOnX=FALSE, alpha = 0.4) +
    geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = F, size = 0.5, alpha = 0.6) + # CI
    geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend = F, size = 1.2) + 
    geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
    geom_point(aes(fill=name), size = 3, shape = 21) + 
    annotate('text', x = 0.93, y = (seq(1, mr2_group_no, 1)+0.35), label= paste("italic(k)==", mr2_mod_table$K), parse=TRUE, hjust = "left", size=3.5) +
    scale_color_manual(values = c("Conventional" = "#CC79A7",  "GLS" = "#D55E00",  "Satellite"= "#0072B2")) +
  scale_fill_manual(values = c("Conventional" = "#CC79A7",  "GLS" = "#D55E00",  "Satellite"= "#0072B2")) +
    ggplot2::theme_bw() +
    ggplot2::guides(fill="none", colour="none") +
    ggplot2::theme(axis.text.y = element_text(size=9, colour="black", hjust=0.5, angle=90),
                   legend.position = "none", axis.title.y = element_blank(), axis.text.x=element_text(size=9)) +
    annotation_custom(grid::rasterGrob(image_satellite), xmin = -0.45, xmax = -0.25, ymin = 2.7, ymax = 3.3) +
    annotation_custom(grid::rasterGrob(image_GLS), xmin = -0.45, xmax = -0.25, ymin = 1.7, ymax = 2.3) +
    annotation_custom(grid::rasterGrob(image_conventional), xmin = -0.45, xmax = -0.25, ymin = 0.7, ymax = 1.3) +
  labs(x="Effect size (ICC)")

# for Figure 2

#c <- ggplot(data = mr2_mod_table, aes(x = estimate, y = name)) + 
#    scale_x_continuous(limits = c(-0.37, 1), breaks = seq(-0.4, 1, by = 0.2), labels = c("-0.4", "-0.2", "0.0", "0.2", "0.4", "0.6", "0.8", "1.0")) + 
#    ggbeeswarm::geom_quasirandom(data = mr2_data, aes(x = yi_ICC, y = moderator, size = scale, colour=moderator), groupOnX=FALSE, alpha = 0.4) +
#    geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = F, size = 0.5, alpha = 0.6) + # CI
#    geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend = F, size = 1.2) + 
#    geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
#    geom_point(aes(fill=name), size = 3, shape = 21) + 
#    annotate('text', x = 0.93, y = (seq(1, mr2_group_no, 1)+0.35), label= paste("italic(k)==", mr2_mod_table$K), parse=TRUE, hjust = "left", size=3.5) +
#    scale_color_manual(values = c("Conventional" = "#CC79A7",  "GLS" = "#D55E00",  "Satellite"= "#0072B2")) +
#  scale_fill_manual(values = c("Conventional" = "#CC79A7",  "GLS" = "#D55E00",  "Satellite"= "#0072B2")) +
#    ggplot2::theme_bw() +
#    ggplot2::guides(fill="none", colour="none") +
#    ggplot2::theme(axis.text.y = element_text(size=9, colour="black", hjust=0.5, angle=90),
#                   legend.position = "none", axis.title = element_blank()) +
#    annotation_custom(grid::rasterGrob(image_satellite), xmin = -0.45, xmax = -0.25, ymin = 2.7, ymax = 3.3) +
#    annotation_custom(grid::rasterGrob(image_GLS), xmin = -0.45, xmax = -0.25, ymin = 1.7, ymax = 2.3) +
#    annotation_custom(grid::rasterGrob(image_conventional), xmin = -0.45, xmax = -0.25, ymin = 0.7, ymax = 1.3)

fig_method

```

**Figure 2c.** Repeatability of avian migration timing for tracking method. The plot shows the group-wise means with 95% confidence intervals (thick lines) and 95% prediction intervals (thin lines), observed effect sizes (back-transformed to ICC) scaled by precision (circles) and k = number of effect sizes.

#### The effect of sex

```{r meta-regression-sex}
# reordering
#df$sex <- factor(df$sex, levels = c("F", "M", "B"))

meta_regression4 <- rma.mv(yi = Zr2, V = VCV, 
                           mods = ~ sex,
                           random = list(~1 | es_ID, 
                                         ~1 | paper_ID, 
                                         ~1 | cohort_ID, 
                                         ~1 | species_ID, 
                                         ~1 | phylogeny),
                           R = list(phylogeny = varcor),
                           data = df)

meta_regression4b <- rma.mv(yi = Zr2, V = VCV, 
                            mods = ~ relevel(sex, ref = "M"),
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor), 
                            data = df)

# Orchard plot - need meta-regression without intercept
meta_regression4c <- rma.mv(yi = Zr2, V = VCV, 
                            mods = ~ sex -1,
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor),
                            data = df)


```

#### Table S6
Regression coefficients (Estimate) and 95% confidence intervals (CIs) both in Zr and back-transformed to ICC from the meta-regression with `sex`. Note that `mu` means the group mean while `beta` represents the contrast between two groups in the Unit column. *R<sup>2</sup><sub>marginal</sub>* = `r round(orchaRd::r2_ml(meta_regression4)[[1]]*100,1)`%. B = both sexes, M = male, and F = female.

```{r sex-table}
# getting marginal R2
r2_meta_regression4 <- r2_ml(meta_regression4)

# getting estimates
# including back-transformation to ICC

mr4 <- mod_results(meta_regression4c, mod="sex")
mr4_mod_table <- mr4$mod_table

mr4_data <- mr4$data

# calculate k for each method separately

#df %>% group_by(sex) %>% summarise(mean(k))


mr4_data <- mr4_data %>% mutate(k = case_when(moderator == "F" ~ 2.37, 
                                              moderator == "M" ~ 2.38,
                                              moderator == "B" ~ 2.82))

mr4_data$yi_ICC <- Zr_to_ICC(mr4_data$yi, mr4_data$k)

mr4_mod_table$k <- c(2.37, 2.38, 2.82)

for(i in names(mr4_mod_table)[2:6]){
  
  mr4_mod_table[i] <- Zr_to_ICC(mr4_mod_table[i], mr4_mod_table$k)
  
}

# creating a table
tibble(
  `Fixed effect` = c(rep(as.character(mr4_mod_table$name),2), cont_gen(mr4_mod_table$name)),
  `Unit` = c(rep(c("Zr (mu)", "ICC (mu)"),each = 3), rep("Zr (beta)", 3)),
  `Estimate` = c(meta_regression4c$b, mr4_mod_table$estimate, 
                 meta_regression4$b[-1], meta_regression4b$b[-(1:2)]),
  `Lower CI [0.025]` = c(meta_regression4c$ci.lb, mr4_mod_table$lowerCL,
                         meta_regression4$ci.lb[-1], meta_regression4b$ci.lb[-(1:2)]),
  `Upper CI  [0.975]` = c(meta_regression4c$ci.ub, mr4_mod_table$upperCL,
                         meta_regression4$ci.ub[-1], meta_regression4b$ci.ub[-(1:2)])) -> t_sex
 # `R2` = c(r2_meta_regression4[1], rep(NA,8))) -> t_sex

t_sex %>% kable("html", digits = 3) %>%
  kable_styling("striped", position = "left")

```

#### Figure 2e
```{r figure2e, fig.width=7, fig.height= 3}

mr4_data$moderator <- factor(mr4_data$moderator, levels = mr4_mod_table$name, labels = mr4_mod_table$name)
mr4_data$scale <- (1/sqrt(mr4_data[,"vi"]))
mr4_mod_table$K <- as.vector(by(mr4_data, mr4_data[,"moderator"], function(x) length(x[,"yi"])))
mr4_group_no <- nrow(mr4_mod_table)

image_male <- readPNG(here("images/Male_icon.png"))
image_female <- readPNG(here("images/Female_icon.png"))
image_both <- readPNG(here("images/Both_sex_icon.png"))

# creating orchaRd plot
# for Figure 2

e <- ggplot(data = mr4_mod_table, aes(x = estimate, y = name)) + 
    scale_x_continuous(limits = c(-0.37, 1), breaks = seq(-0.4, 1, by = 0.2), labels = c("-0.4", "-0.2", "0.0", "0.2", "0.4", "0.6", "0.8", "1.0")) +  
    ggbeeswarm::geom_quasirandom(data = mr4_data, aes(x = yi_ICC, y = moderator, size = scale, colour=moderator), groupOnX=FALSE, alpha = 0.4) +
    geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = F, size = 0.5, alpha = 0.6) + # CI
    geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend = F, size = 1.2) + 
    geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
    geom_point(aes(fill=name), size = 3, shape = 21) + 
    annotate('text', x = 0.93, y = (seq(1, mr4_group_no, 1)+0.35), label= paste("italic(k)==", mr4_mod_table$K), parse=TRUE, hjust = "left", size=3.5) +
    ggplot2::theme_bw() +
    ggplot2::guides(fill="none", colour="none") +
    ggplot2::theme(axis.text.y = element_text(size=9, colour="black", hjust=0.5, angle=90),
                   legend.position = "none", axis.title.y = element_blank()) +
    ggplot2::labs(x=paste("Effect size (ICC)")) +
    scale_y_discrete(labels=c("F" = "Female", "M" = "Male", "B" = "Both")) +
    	      scale_fill_manual(values=cbpl) +
	      scale_colour_manual(values=cbpl) +
        annotation_custom(grid::rasterGrob(image_male), xmin = -0.45, xmax = -0.25, ymin = 2.7, ymax = 3.3) +
    annotation_custom(grid::rasterGrob(image_female), xmin = -0.45, xmax = -0.25, ymin = 1.7, ymax = 2.3) +
    annotation_custom(grid::rasterGrob(image_both), xmin = -0.45, xmax = -0.25, ymin = 0.7, ymax = 1.3)

e

```

**Figure 2e.** Repeatability of avian migration timing for sex. The plot shows the group-wise means with 95% confidence intervals (thick lines) and 95% prediction intervals (thin lines), observed effect sizes (back-transformed to ICC) scaled by precision (circles) and k = number of effect sizes.


#### The effect of ecological group

```{r meta-regression-taxa}
meta_regression3 <- rma.mv(yi = Zr2, V = VCV, 
                           mods = ~ taxa,
                           random = list(~1 | es_ID, 
                                         ~1 | paper_ID, 
                                         ~1 | cohort_ID, 
                                         ~1 | species_ID, 
                                         ~1 | phylogeny),
                           R = list(phylogeny = varcor), 
                           data = df)

# reordering
#df$taxa <- factor(df$taxa, levels = c("Waterbird", "Seabird", "Landbird"))

meta_regression3b <- rma.mv(yi = Zr2, V = VCV, 
                           mods = ~ relevel(taxa, ref = "Waterbird"),
                           random = list(~1 | es_ID, 
                                         ~1 | paper_ID, 
                                         ~1 | cohort_ID, 
                                         ~1 | species_ID, 
                                         ~1 | phylogeny),
                           R = list(phylogeny = varcor), 
                           data = df)

# Orchard plot - need meta-regression without intercept
meta_regression3c <- rma.mv(yi = Zr2, V = VCV, 
                            mods = ~ taxa -1,
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor),
                            data = df)

```

#### Table S7
Regression coefficients (Estimate) and 95% confidence intervals (CIs) both in Zr and back-transformed to ICC from the meta-regression with `taxa`. Note that `mu` means the group mean while `beta` represents the contrast between two groups in the Unit column. *R<sup>2</sup><sub>marginal</sub>* = `r round(orchaRd::r2_ml(meta_regression3)[[1]]*100,1)`%. 

```{r taxa-table}
# getting marginal R2
r2_meta_regression3 <- r2_ml(meta_regression3)

# getting estimates
# including back-transformation to ICC

mr3 <- mod_results(meta_regression3c, mod="taxa")
mr3_mod_table <- mr3$mod_table

mr3_data <- mr3$data

# calculate k for each method separately

#df %>% group_by(taxa) %>% summarise(mean(k))

mr3_data <- mr3_data %>% mutate(k = case_when(moderator == "Landbird" ~ 2.68, 
                                              moderator == "Seabird" ~ 2.38,
                                              moderator == "Waterbird" ~ 3.08))

mr3_data$yi_ICC <- Zr_to_ICC(mr3_data$yi, mr3_data$k)

mr3_mod_table$k <- c(2.68, 2.38, 3.08)

for(i in names(mr3_mod_table)[2:6]){
  
  mr3_mod_table[i] <- Zr_to_ICC(mr3_mod_table[i], mr3_mod_table$k)
  
}

# creating a table
tibble(
  `Fixed effect` = c(rep(as.character(mr3_mod_table$name),2), cont_gen(mr3_mod_table$name)),
  `Unit` = c(rep(c("Zr (mu)", "ICC (mu)"),each = 3), rep("Zr (beta)", 3)),
  `Estimate` = c(meta_regression3c$b, mr3_mod_table$estimate, 
                 meta_regression3$b[-1], meta_regression3b$b[-(1:2)]),
  `Lower CI [0.025]` = c(meta_regression3c$ci.lb, mr3_mod_table$lowerCL,
                         meta_regression3$ci.lb[-1], meta_regression3b$ci.lb[-(1:2)]),
  `Upper CI  [0.975]` = c(meta_regression3c$ci.ub, mr3_mod_table$upperCL,
                         meta_regression3$ci.ub[-1], meta_regression3b$ci.ub[-(1:2)])) -> t_method
 # `R2` = c(r2_meta_regression3[1], rep(NA,8))) -> t_method

t_method %>% kable("html", digits = 3) %>%
  kable_styling("striped", position = "left") 

```

#### Figure 2d
```{r figure2d, fig.width=7, fig.height= 3}

mr3_data$moderator <- factor(mr3_data$moderator, levels = mr3_mod_table$name, labels = mr3_mod_table$name)
mr3_data$scale <- (1/sqrt(mr3_data[,"vi"]))
mr3_mod_table$K <- as.vector(by(mr3_data, mr3_data[,"moderator"], function(x) length(x[,"yi"])))
mr3_group_no <- nrow(mr3_mod_table)

image_waterbird <- readPNG(here("images/Waterbird_icon.png"))
image_seabird <- readPNG(here("images/Seabird_icon.png"))
image_landbird <- readPNG(here("images/Landbird_icon.png"))

# creating orchaRd plot

fig_eco_group <- ggplot(data = mr3_mod_table, aes(x = estimate, y = name)) + 
    scale_x_continuous(limits = c(-0.37, 1), breaks = seq(-0.4, 1, by = 0.2), labels = c("-0.4", "-0.2", "0.0", "0.2", "0.4", "0.6", "0.8", "1.0")) + 
    ggbeeswarm::geom_quasirandom(data = mr3_data, aes(x = yi_ICC, y = moderator, size = scale, colour=moderator), groupOnX=FALSE, alpha = 0.4) +
    geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = F, size = 0.5, alpha = 0.6) + # CI
    geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend = F, size = 1.2) + 
    geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
    geom_point(aes(fill=name), size = 3, shape = 21) + 
    annotate('text', x = 0.93, y = (seq(1, mr3_group_no, 1)+0.35), label= paste("italic(k)==", mr3_mod_table$K), parse=TRUE, hjust = "left", size=3.5) +
    ggplot2::theme_bw() +
    ggplot2::guides(fill="none", colour="none") +
    ggplot2::theme(axis.text.y = element_text(size=9, colour="black", hjust=0.5, angle=90),
                   legend.position = "none", axis.title.y = element_blank(), axis.text.x=element_text(size=9)) +
    # setting colours
  scale_color_manual(values = c("Seabird" = "#009E73",  "Landbird" = "#D55E00",  "Waterbird"= "#0072B2")) +
  scale_fill_manual(values = c("Seabird" = "#009E73",  "Landbird" = "#D55E00",  "Waterbird"= "#0072B2")) +
    annotation_custom(grid::rasterGrob(image_waterbird), xmin = -0.45, xmax = -0.25, ymin = 2.7, ymax = 3.3) +
    annotation_custom(grid::rasterGrob(image_seabird), xmin = -0.45, xmax = -0.25, ymin = 1.7, ymax = 2.3) +
    annotation_custom(grid::rasterGrob(image_landbird), xmin = -0.45, xmax = -0.25, ymin = 0.7, ymax = 1.3) +
  labs(x="Effect size (ICC)")

fig_eco_group

# for Figure 2

d <- ggplot(data = mr3_mod_table, aes(x = estimate, y = name)) + 
    scale_x_continuous(limits = c(-0.37,1), breaks = seq(-0.4, 1, by = 0.2), labels = c("-0.4","-0.2", "0.0", "0.2", "0.4", "0.6", "0.8", "1.0")) + 
    ggbeeswarm::geom_quasirandom(data = mr3_data, aes(x = yi_ICC, y = moderator, size = scale, colour=moderator), groupOnX=FALSE, alpha = 0.4) +
    geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = F, size = 0.5, alpha = 0.6) + # CI
    geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend = F, size = 1.2) + 
    geom_vline(xintercept = 0, linetype = 2, colour = "black", alpha = 0.5) + # creating dots and different size (bee-swarm and bubbles)
    geom_point(aes(fill=name), size = 3, shape = 21) + 
    #ggplot2::annotate("text", x = max(mr2_data$yi_ICC) + (max(mr2_data$yi_ICC)*0.10), y = (seq(1, mr2_group_no, 1)+0.3),label = paste("italic(k)==", mr2_mod_table$K), parse = TRUE, hjust = "right", size = 3.5) +
    annotate('text', x = 0.93, y = (seq(1, mr3_group_no, 1)+0.35), label= paste("italic(k)==", mr3_mod_table$K), parse=TRUE, hjust = "left", size=3.5) +
    ggplot2::theme_bw() +
    ggplot2::guides(fill="none", colour="none") +
    ggplot2::theme(axis.text.y = element_text(size=9, colour="black", hjust=0.5, angle=90),
                   legend.position = "none", axis.title = element_blank()) +
    # setting colours
  scale_color_manual(values = c("Seabird" = "#009E73",  "Landbird" = "#D55E00",  "Waterbird"= "#0072B2")) +
  scale_fill_manual(values = c("Seabird" = "#009E73",  "Landbird" = "#D55E00",  "Waterbird"= "#0072B2")) +
    annotation_custom(grid::rasterGrob(image_waterbird), xmin = -0.45, xmax = -0.25, ymin = 2.7, ymax = 3.3) +
    annotation_custom(grid::rasterGrob(image_seabird), xmin = -0.45, xmax = -0.25, ymin = 1.7, ymax = 2.3) +
    annotation_custom(grid::rasterGrob(image_landbird), xmin = -0.45, xmax = -0.25, ymin = 0.7, ymax = 1.3)

```

**Figure 2d.** Repeatability of avian migration timing for ecological group. The plot shows the group-wise means with 95% confidence intervals (thick lines) and 95% prediction intervals (thin lines), observed effect sizes (back-transformed to ICC) scaled by precision (circles) and k = number of effect sizes.

#### Putting together Figure 2

```{r fig2}

# building fig 2 using patchwork

#Figure2 <- (a / b / c / d / e + plot_layout(heights = c(1.8, 3.7, 3, 3, 3)) + plot_annotation(tag_levels = 'a', tag_suffix = ')'))

#Figure2

#ggsave(here('figs/Figure_2_R_zero.pdf'), width = 180, height = 300, units=c('mm'), dpi=600) 
#edited this file in Affinity Designer

```

![](C:/Users/rst18vzu/OneDrive - University of East Anglia/PhD/Lit. review/Meta-analysis/Avian_migration_meta-analysis/figs/Figure_2_R_zero_final.jpg){width=80%}

**Figure 2.** Putting all five panels together: Figure 2a - Figure 2e (see the main text). Note this plot was edited in Affinity Designer after construction in R.

#### The effect of number of observations per individual (k)

```{r meta-regression-k}

meta_regression5 <- rma.mv(yi = Zr2, V = VCV, 
                            mods = ~ k,
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor),
                            data = df)
```

#### Table S8
Regression coefficients (Estimate) and 95% confidence intervals (CIs) in Zr from the meta-regression with `k` (number of observations per individual). *R<sup>2</sup><sub>marginal</sub>* = `r round(orchaRd::r2_ml(meta_regression5)[[1]]*100,1)`%.

```{r k-table}
# getting marginal R2
r2_meta_regression5 <- r2_ml(meta_regression5)

# creating a table
tibble(
  `Fixed effect` = c("Intercept", "k"),
  `Estimate` = c(meta_regression5$b),
  `Lower CI [0.025]` = c(meta_regression5$ci.lb),
  `Upper CI  [0.975]` = c(meta_regression5$ci.ub)) -> t_k
#  `R2` = c(r2_meta_regression5[1], NA)) -> t_k

t_k %>% kable("html", digits = 3) %>%
  kable_styling("striped", position = "left") 

```

#### Figure S4
```{r figureS4, fig.width=7, fig.height= 4}
pred_meta_regression5 <- predict.rma(meta_regression5)

# creating bubble plot
df %>% mutate(fit=pred_meta_regression5$pred, 
               ci.lb=pred_meta_regression5$ci.lb,
               ci.ub=pred_meta_regression5$ci.ub,
               pr.lb=pred_meta_regression5$cr.lb,
               pr.ub=pred_meta_regression5$cr.ub) %>% 
ggplot(aes(x = k, y = Zr2)) +
     geom_point(aes(size=(1/sqrt(VZr))), shape=21, alpha=0.5, fill = "grey85", colour="grey60", col="gray25",stroke=1) +
     geom_line(aes(y = fit), size = 1.5, colour="darkorchid4")+
  geom_ribbon(aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = .3, fill="darkorchid4") +
  labs(x = "Number of observations per individual (k)", y = "Effect size (Zr)", size = "Precison (1/SE)") +
  theme_bw() +
  scale_size_continuous(range=c(1,12))+
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5) +
  theme(text = element_text(size = 9, colour = "black", hjust = 0.5),
          legend.text=element_text(size=8),
          legend.position=c(1,1), 
          legend.justification = c(1,1),
          legend.background = element_blank(), 
          legend.direction="horizontal",
          legend.title = element_text(size=8)
        )

```

**Figure S4.** Repeatability of avian migration timing for the continuous variable `k`, where the solid line represents the model estimate and the shading shows the 95% confidence intervals, with individual data points scaled by precision (1/SE).

```{r meta-regression-outlier-k}
df2 <- df %>% filter(k < 10)
VCV2 <- impute_covariance_matrix(vi = df2$VZr, cluster = df2$cohort_ID, r = 0.5)

meta_regression6 <- rma.mv(yi = Zr2, V = VCV2, 
                           mods = ~ k,
                           random = list(~1 | es_ID, 
                                         ~1 | paper_ID, 
                                         ~1 | cohort_ID, 
                                         ~1 | species_ID, 
                                         ~1 | phylogeny),
                           R = list(phylogeny = varcor), # added in phylogney
                           data = df2)
```

When we remove the two points with high `k` values (k = 12.4), the results are similar. There is no significant effect (slope = `r round(meta_regression6$b[2,1],3)`, 95% CI = [`r round(meta_regression6$ci.lb[2],3)`, `r round(meta_regression6$ci.ub[2],3)`]; *R<sup>2</sup><sub>marginal</sub>* = `r round(orchaRd::r2_ml(meta_regression6)[[1]]*100,1)`%) of 'k' on effect sizes, showing that repeatability does not vary with the number of observations per individual.


### Model selection (multi-predictor model)

Here we used the `MuMin` package to generate all possible moderator combinations (using all five variables: `annual_event`, `method`, `sex`, `taxa` & `k` (number of observations per individuals)), determine the importance of the moderators, and generate model-averaged estimates.

```{r model-selection}
eval(metafor:::.MuMIn)

full_model_MuMIn <- rma.mv(yi = Zr2, V = VCV, 
                          mods = ~ method + 
                            taxa + 
                            sex + 
                            annual_event +
                            k, 
                          random = list(~1 | es_ID, 
                                        ~1 | paper_ID, 
                                        ~1 | cohort_ID, 
                                        ~1 | species_ID, 
                                        ~1 | phylogeny),
                          R = list(phylogeny = varcor), 
                          method = "ML", # maximum likelihood for model selection
                          data = df)

# vif.rma(full_model_MuMIn)  # No major problems of collinearity (VIF <4)

candidate_models<-dredge(full_model_MuMIn) # Generate all possible combinations of moderators

candidates_aic2 <- subset(candidate_models, delta<=2) # Display all models within 2 values of AICc

importance <- sw(model.avg(candidate_models, subset=delta<=2))# relative importance (sum of weights) of the moderators

mod.avg <- summary(model.avg(candidate_models, subset=delta<=2)) # Generate model-averaged estimates 

confidence <- confint(mod.avg, full=TRUE) # Generate confidence intervals for the estimates averaged using full-averages procedures

```

#### Table S9
The top five models within the $\Delta$AIC difference of 2, and which five variables: `annual_event`, `method`, `taxa`, `sex`, & `k` were included (indicated by $+$); model weights and the sum of weights for each of the variables are included. 

```{r model-selection-table}
# creating a table
tibble(
  `Model (variable weight)` = c("Model1", "Model2", "Model3", "Model4", "Model5", "(Sum of weights)"),
  annual_event = c(if_else(candidates_aic2$annual_event == "+", "$+$", "NA"),round(importance[1],3) ),
  taxa =  c(if_else(candidates_aic2$taxa== "+", "$+$", "NA"),round(importance[2], 3)),
  method = c(if_else(candidates_aic2$method== "+", "$+$", "NA"),round(importance[3], 3)),
  k = c(if_else(candidates_aic2$k <= 0, "$+$", "NA"),round(importance[4], 3)),
  sex = c(if_else(candidates_aic2$sex== "+", "$+$", "NA"),round(importance[5], 3)),
  delta_AICc = c(candidates_aic2$delta, NA),
  Weight = c(candidates_aic2$weight, NA)) %>% 
  kable("html", digits = 3) %>%
  kable_styling("striped", position = "left")
```

#### Model averaging

#### Table S10
The average estimates for regression coefficients (Estimate) and 95% confidence intervals (CIs) from the model averaging procedure using full-averages (assuming zero values for moderators when they do not occur).
```{r model-averaging}
# creating a table
tibble(
  `Fixed effect` = c("Intercept",
                     "Depart_breed", "Nonbreed_arrival", "Nonbreed_depart", 
                     "Female","Male", "Seabird", "Waterbird", "GLS", "Satellite",
                     "k"),
  Estimate = mod.avg$coefmat.full[,1],
  `Lower CI [0.025]` = confidence[,1],
  `Upper CI  [0.975]` =  confidence[,2]) %>% 
  kable("html", digits = 3) %>%
  kable_styling("striped", position = "left")
```

## Sensitivity Analysis

### Variance-covariance matrix with different levels of correlation
Multiple repeatability estimates were measured on the same animals within a paper (cohort ID) which induces a correlation between sampling error variances [@noble_nonindependence_2017]. Thus, we constructed variance-covariance matrices to model shared sampling error for effect sizes from the same cohort. We initially assumed a 0.5 correlation, but also ran the phylogenetic meta-analytic model with a 0.25 and 0.75 correlation. All three correlations yielded qualitatively similar results, thus throughout the manuscript we assume a 0.5 correlation, but the results for the other correlation values are presented below. 

#### Table S11
Overall effects (meta-analytic means) and 95% confidence intervals (CIs) in *Zr* and heterogeneity, *I*^2^, for the phylogenetic multilevel intercept-only meta-analysis model when testing different levels of correlation (r = 0.25, 0.50, and 0.75) between sampling variances from the same cohort of birds.

```{r VCV-cor}
# Create a variance-covariance matrix at the cohort level with different correlation values
VCV_25 <- impute_covariance_matrix(vi = df$VZr, cluster = df$cohort_ID, r = 0.25)
VCV_75 <- impute_covariance_matrix(vi = df$VZr, cluster = df$cohort_ID, r = 0.75)

# Run phylogenetic meta-analytic models with 0.25 and 0.75 VCV
ma_model2_VCV25 <- rma.mv(yi = Zr2, V = VCV_25, 
                      random = list(~1 | es_ID, 
                                    ~1 | paper_ID, 
                                    ~1 | cohort_ID, 
                                    ~1 | species_ID, # non-phylo effect
                                    ~1 | phylogeny), # phylo effect
                      R = list(phylogeny = varcor), # phylogenetic relatedness
                      data = df)

i2_ma2_VCV25 <- round(i2_ml(ma_model2_VCV25)*100,1)

ma_model2_VCV75 <- rma.mv(yi = Zr2, V = VCV_75, 
                      random = list(~1 | es_ID, 
                                    ~1 | paper_ID, 
                                    ~1 | cohort_ID, 
                                    ~1 | species_ID, # non-phylo effect
                                    ~1 | phylogeny), # phylo effect
                      R = list(phylogeny = varcor), # phylogenetic relatedness
                      data = df)

i2_ma2_VCV75 <- round(i2_ml(ma_model2_VCV75)*100,1)

# creating a table
tibble(
  Model = c("Meta-analysis, r=0.25", "Meta-analysis, r=0.50", "Meta-analysis, r=0.75"),
  `Overall mean` = c(ma_model2_VCV25$b, ma_model2$b, ma_model2_VCV75$b),
  `Lower CI [0.025]` = c(ma_model2_VCV25$ci.lb, ma_model2$ci.lb, ma_model2_VCV75$ci.lb),
  `Upper CI [0.975]` = c(ma_model2_VCV25$ci.ub, ma_model2$ci.ub, ma_model2_VCV75$ci.ub),
  `I^2^~total~` = c(i2_ma2_VCV25[1], i2_ma2[1], i2_ma2_VCV75[1]),
  `I^2^~es~` = c(i2_ma2_VCV25[2], i2_ma2[2], i2_ma2_VCV75[2]),
  `I^2^~paper~` = c(i2_ma2_VCV25[3], i2_ma2[3], i2_ma2_VCV75[3]),
  `I^2^~cohort~` = c(i2_ma2_VCV25[4], i2_ma2[4], i2_ma2_VCV75[4]),
  `I^2^~species~` = c(i2_ma2_VCV25[5], i2_ma2[5], i2_ma2_VCV75[5]),
  `I^2^~phylo~` = c(i2_ma2_VCV25[6], i2_ma2[6], i2_ma2_VCV75[6]),) %>% 
  kable("html",  digits = 3) %>%
  kable_styling("striped", position = "left")

```

### Inclusion of negative repeatabiliy estimates
Correlation- and ANOVA-based repeatabilities can produce negative values, often reflecting noise around a statistical zero [@nakagawa_repeatability_2010]. For our main analyses, we set these negative estimates to zero, however here, we re-ran all the meta-analytic and meta-regression models with these negative values included.

#### Table S12
Overall effects (meta-analytic means) and 95% confidence intervals (CIs) both in *Zr* and back-transformed to *ICC*, and heterogeneity, *I*^2^, for the multilevel intercept-only meta-analysis models including and excluding phylogeny when negative repeatability values are included.

```{r meta-analytic-mods-neg}

ma_model1_neg <- rma.mv(yi = Zr, V = VCV,
                    random = list(~1 | es_ID, 
                                  ~1 | paper_ID, 
                                  ~1 | cohort_ID, 
                                  ~1 | species_ID), 
                    data = df)

ma_model2_neg <- rma.mv(yi = Zr, V = VCV, 
                    random = list(~1 | es_ID, 
                                  ~1 | paper_ID, 
                                  ~1 | cohort_ID, 
                                  ~1 | species_ID, # non-phylo effect
                                  ~1 | phylogeny), # phylo effect
                    R = list(phylogeny = varcor), # phylogenetic relatedness
                    data = df)

## # estimating I2 as measure of heterogeneity
i2_ma1_neg <- round(i2_ml(ma_model1_neg)*100,1)
i2_ma2_neg <- round(i2_ml(ma_model2_neg)*100,1)

# Back-transform to ICC
ma1_neg <- mod_results(ma_model1_neg, mod="Int")
ma1_mod_table_neg <- ma1_neg$mod_table

ma2_neg <- mod_results(ma_model2_neg, mod="Int")
ma2_mod_table_neg <- ma2_neg$mod_table

# need to calculate k for whole data set to use in formula

k_all <- mean(df$k)

for(i in names(ma1_mod_table_neg)[2:6]){
  
  ma1_mod_table_neg[i] <- Zr_to_ICC(ma1_mod_table_neg[i], k_all)
  
}

for(i in names(ma2_mod_table_neg)[2:6]){
  
  ma2_mod_table_neg[i] <- Zr_to_ICC(ma2_mod_table_neg[i], k_all)
  
}

# creating a table
tibble(
  Model = c("Meta-analysis (Zr)", "Meta-analysis (ICC)", "Meta-analysis phylo (Zr)", "Meta-analysis phylo (ICC)"),
  `Overall mean` = c(ma_model1_neg$b, ma1_mod_table_neg$estimate, ma_model2_neg$b, ma2_mod_table_neg$estimate),
  `Lower CI [0.025]` = c(ma_model1_neg$ci.lb, ma1_mod_table_neg$lowerCL, ma_model2_neg$ci.lb, ma2_mod_table_neg$lowerCL),
  `Upper CI [0.975]` = c(ma_model1_neg$ci.ub, ma1_mod_table_neg$upperCL, ma_model2_neg$ci.ub, ma2_mod_table_neg$upperCL),
  `I^2^~total~` = c(i2_ma1_neg[1], NA, i2_ma2_neg[1], NA),
  `I^2^~es~` = c(i2_ma1_neg[2], NA, i2_ma2_neg[2], NA),
  `I^2^~paper~` = c(i2_ma1_neg[3], NA, i2_ma2_neg[3], NA),
  `I^2^~cohort~` = c(i2_ma1_neg[4], NA, i2_ma2_neg[4], NA),
  `I^2^~species~` = c(i2_ma1_neg[5], NA, i2_ma2_neg[5], NA),
  `I^2^~phylo~` = c(NA, NA, i2_ma2_neg[6], NA),) %>% 
  kable("html",  digits = 3) %>%
  kable_styling("striped", position = "left")
```

```{r meta-regression-annual-event-neg}
# meta-regression: mutiple intercepts
meta_regression1_neg <- rma.mv(yi = Zr, V = VCV, 
                           mods = ~ annual_event,
                           random = list(~1 | es_ID, 
                                         ~1 | paper_ID, 
                                         ~1 | cohort_ID, 
                                         ~1 | species_ID, 
                                         ~1 | phylogeny),
                           R = list(phylogeny = varcor), # added in phylogney
                           data = df)

meta_regression1b_neg <- rma.mv(yi = Zr, V = VCV, 
                            mods = ~ relevel(annual_event, ref = "Depart_breed"),
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor), # added in phylogney
                            data = df)


meta_regression1c_neg <- rma.mv(yi = Zr, V = VCV, 
                            mods = ~ relevel(annual_event, ref = "Nonbreed_depart"),
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor), # added in phylogney
                            data = df)

# meta-regression: contrast (for orchard plot)
meta_regression1d_neg <- rma.mv(yi = Zr, V = VCV, 
                            mods = ~ annual_event -1,
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor), # added in phylogney
                            data = df)
```

#### Table S13
Regression coefficients (Estimate) and 95% confidence intervals (CIs) both in Zr and back-transformed to ICC from the meta-regression with `annual_event` when negative repeatability values are included. Note that `mu` means the group mean while `beta` represents the contrast between two groups in the Unit column. *R<sup>2</sup><sub>marginal</sub>* = `r round(orchaRd::r2_ml(meta_regression1_neg)[[1]]*100,1)`%.

```{r annual-event-table-neg}

# getting marginal R2
r2_meta_regression1_neg <- r2_ml(meta_regression1_neg)

# getting estimates
# including back-transformation to ICC

mr1_neg <- mod_results(meta_regression1d_neg, mod="annual_event")
mr1_mod_table_neg <- mr1_neg$mod_table

mr1_data_neg <- mr1_neg$data

# calculate k for each method separately

# df %>% group_by(annual_event) %>% summarise(mean(k))

mr1_data_neg <- mr1_data_neg %>% mutate(k = case_when(moderator == "Arrival_breed" ~ 2.55, 
                                              moderator == "Depart_breed" ~ 2.45,
                                              moderator == "Nonbreed_arrival" ~ 3.20,
                                              moderator == "Nonbreed_depart" ~ 2.94))

mr1_data_neg$yi_ICC <- Zr_to_ICC(mr1_data_neg$yi, mr1_data_neg$k)

mr1_mod_table_neg$k <- c(2.55, 2.45, 3.20, 2.94)

for(i in names(mr1_mod_table_neg)[2:6]){
  
  mr1_mod_table_neg[i] <- Zr_to_ICC(mr1_mod_table_neg[i], mr1_mod_table_neg$k)
  
}

# creating a table
tibble(
  `Fixed effect` = c(rep(as.character(mr1_mod_table_neg$name),2), cont_gen(mr1_mod_table_neg$name)),
  `Unit` = c(rep(c("Zr (mu)", "ICC (mu)"),each = 4), rep("Zr (beta)", 6)),
  `Estimate` = c(meta_regression1d_neg$b, mr1_mod_table_neg$estimate, 
                 meta_regression1_neg$b[-1], meta_regression1b_neg$b[-(1:2)], meta_regression1c_neg$b[-(1:3)]),
  `Lower CI [0.025]` = c(meta_regression1d_neg$ci.lb, mr1_mod_table_neg$lowerCL,
                         meta_regression1_neg$ci.lb[-1], meta_regression1b_neg$ci.lb[-(1:2)], meta_regression1c_neg$ci.lb[-(1:3)]),
  `Upper CI  [0.975]` = c(meta_regression1d_neg$ci.ub, mr1_mod_table_neg$upperCL,
                         meta_regression1_neg$ci.ub[-1], meta_regression1b_neg$ci.ub[-(1:2)], meta_regression1c_neg$ci.ub[-(1:3)])) -> t_annual_event_neg
 # `R2` = c(r2_meta_regression1[1], rep(NA,13))) -> 

t_annual_event_neg %>% kable("html", digits = 3) %>%
  kable_styling("striped", position = "left")
               
```

```{r meta-regression-method-neg}
meta_regression2_neg <- rma.mv(yi = Zr, V = VCV, 
                      mods = ~ method,
                      random = list(~1 | es_ID, 
                                    ~1 | paper_ID, 
                                    ~1 | cohort_ID, 
                                    ~1 | species_ID, 
                                    ~1 | phylogeny),
                      R = list(phylogeny = varcor),
                      data = df)

# reordering
# df$method <- factor(df$method, levels = c("Conventional", "GLS", "Satellite"))

meta_regression2b_neg <- rma.mv(yi = Zr, V = VCV, 
                            mods = ~ relevel(method, ref = "GLS"),
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor),
                            data = df)

# Orchard plot - need meta-regression without intercept
meta_regression2c_neg <- rma.mv(yi = Zr, V = VCV, 
                            mods = ~ method -1,
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor),
                            data = df)
```

#### Table S14
Regression coefficients (Estimate) and 95% confidence intervals (CIs) both in Zr and back-transformed to ICC from the meta-regression with `method` when negative repeatability values are included. Note that `mu` means the group mean while `beta` represents the contrast between two groups in the Unit column. *R<sup>2</sup><sub>marginal</sub>* = `r round(orchaRd::r2_ml(meta_regression2_neg)[[1]]*100,1)`%.

```{r method-table-neg}
# getting marginal R2
r2_meta_regression2_neg <- r2_ml(meta_regression2_neg)

# getting estimates
# including back-transformation to ICC

mr2_neg <- mod_results(meta_regression2c_neg, mod="method")
mr2_mod_table_neg <- mr2_neg$mod_table

mr2_data_neg <- mr2_neg$data

# calculate k for each method separately

#df %>% group_by(method) %>% summarise(mean(k))


mr2_data_neg <- mr2_data_neg %>% mutate(k = case_when(moderator == "GLS" ~ 2.20, 
                                              moderator == "Conventional" ~ 3.13,
                                              moderator == "Satellite" ~ 3.28))

mr2_data_neg$yi_ICC <- Zr_to_ICC(mr2_data_neg$yi, mr2_data_neg$k)

mr2_mod_table_neg$k <- c(3.13, 2.20, 3.28)

for(i in names(mr2_mod_table_neg)[2:6]){
  
  mr2_mod_table_neg[i] <- Zr_to_ICC(mr2_mod_table_neg[i], mr2_mod_table_neg$k)
  
}

# creating a table
tibble(
  `Fixed effect` = c(rep(as.character(mr2_mod_table_neg$name),2), cont_gen(mr2_mod_table_neg$name)),
  `Unit` = c(rep(c("Zr (mu)", "ICC (mu)"),each = 3), rep("Zr (beta)", 3)),
  `Estimate` = c(meta_regression2c_neg$b, mr2_mod_table_neg$estimate, 
                 meta_regression2_neg$b[-1], meta_regression2b_neg$b[-(1:2)]),
  `Lower CI [0.025]` = c(meta_regression2c_neg$ci.lb, mr2_mod_table_neg$lowerCL,
                         meta_regression2_neg$ci.lb[-1], meta_regression2b_neg$ci.lb[-(1:2)]),
  `Upper CI  [0.975]` = c(meta_regression2c_neg$ci.ub, mr2_mod_table_neg$upperCL,
                         meta_regression2_neg$ci.ub[-1], meta_regression2b_neg$ci.ub[-(1:2)])) -> t_method_neg

t_method_neg %>% kable("html", digits = 3) %>%
  kable_styling("striped", position = "left") 
```

```{r meta-regression-sex-neg}
# reordering
#df$sex <- factor(df$sex, levels = c("F", "M", "B"))

meta_regression4_neg <- rma.mv(yi = Zr, V = VCV, 
                           mods = ~ sex,
                           random = list(~1 | es_ID, 
                                         ~1 | paper_ID, 
                                         ~1 | cohort_ID, 
                                         ~1 | species_ID, 
                                         ~1 | phylogeny),
                           R = list(phylogeny = varcor),
                           data = df)

meta_regression4b_neg <- rma.mv(yi = Zr, V = VCV, 
                            mods = ~ relevel(sex, ref = "M"),
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor), 
                            data = df)

# Orchard plot - need meta-regression without intercept
meta_regression4c_neg <- rma.mv(yi = Zr, V = VCV, 
                            mods = ~ sex -1,
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor),
                            data = df)
```

#### Table S15
Regression coefficients (Estimate) and 95% confidence intervals (CIs) both in Zr and back-transformed to ICC from the meta-regression with `sex` when negative repeatability values are included. Note that `mu` means the group mean while `beta` represents the contrast between two groups in the Unit column. *R<sup>2</sup><sub>marginal</sub>* = `r round(orchaRd::r2_ml(meta_regression4_neg)[[1]]*100,1)`%. B = both sexes, M = male, and F = female.

```{r sex-table-neg}

# getting marginal R2
r2_meta_regression4_neg <- r2_ml(meta_regression4_neg)

# getting estimates
# including back-transformation to ICC

mr4_neg <- mod_results(meta_regression4c_neg, mod="sex")
mr4_mod_table_neg <- mr4_neg$mod_table

mr4_data_neg <- mr4_neg$data

# calculate k for each method separately

#df %>% group_by(sex) %>% summarise(mean(k))


mr4_data_neg <- mr4_data_neg %>% mutate(k = case_when(moderator == "F" ~ 2.37, 
                                              moderator == "M" ~ 2.38,
                                              moderator == "B" ~ 2.82))

mr4_data_neg$yi_ICC <- Zr_to_ICC(mr4_data_neg$yi, mr4_data_neg$k)

mr4_mod_table_neg$k <- c(2.37, 2.38, 2.82)

for(i in names(mr4_mod_table_neg)[2:6]){
  
  mr4_mod_table_neg[i] <- Zr_to_ICC(mr4_mod_table_neg[i], mr4_mod_table_neg$k)
  
}

# creating a table
tibble(
  `Fixed effect` = c(rep(as.character(mr4_mod_table_neg$name),2), cont_gen(mr4_mod_table_neg$name)),
  `Unit` = c(rep(c("Zr (mu)", "ICC (mu)"),each = 3), rep("Zr (beta)", 3)),
  `Estimate` = c(meta_regression4c_neg$b, mr4_mod_table_neg$estimate, 
                 meta_regression4_neg$b[-1], meta_regression4b_neg$b[-(1:2)]),
  `Lower CI [0.025]` = c(meta_regression4c_neg$ci.lb, mr4_mod_table_neg$lowerCL,
                         meta_regression4_neg$ci.lb[-1], meta_regression4b_neg$ci.lb[-(1:2)]),
  `Upper CI  [0.975]` = c(meta_regression4c_neg$ci.ub, mr4_mod_table_neg$upperCL,
                         meta_regression4_neg$ci.ub[-1], meta_regression4b_neg$ci.ub[-(1:2)])) -> t_sex_neg

t_sex_neg %>% kable("html", digits = 3) %>%
  kable_styling("striped", position = "left")

```

```{r meta-regression-taxa-neg}
meta_regression3_neg <- rma.mv(yi = Zr, V = VCV, 
                           mods = ~ taxa,
                           random = list(~1 | es_ID, 
                                         ~1 | paper_ID, 
                                         ~1 | cohort_ID, 
                                         ~1 | species_ID, 
                                         ~1 | phylogeny),
                           R = list(phylogeny = varcor), 
                           data = df)

# reordering
#df$taxa <- factor(df$taxa, levels = c("Waterbird", "Seabird", "Landbird"))

meta_regression3b_neg <- rma.mv(yi = Zr, V = VCV, 
                           mods = ~ relevel(taxa, ref = "Waterbird"),
                           random = list(~1 | es_ID, 
                                         ~1 | paper_ID, 
                                         ~1 | cohort_ID, 
                                         ~1 | species_ID, 
                                         ~1 | phylogeny),
                           R = list(phylogeny = varcor), 
                           data = df)

# Orchard plot - need meta-regression without intercept
meta_regression3c_neg <- rma.mv(yi = Zr, V = VCV, 
                            mods = ~ taxa -1,
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor),
                            data = df)
```

#### Table S16
Regression coefficients (Estimate) and 95% confidence intervals (CIs) both in Zr and back-transformed to ICC from the meta-regression with `taxa` when negative repeatability values are included. Note that `mu` means the group mean while `beta` represents the contrast between two groups in the Unit column. *R<sup>2</sup><sub>marginal</sub>* = `r round(orchaRd::r2_ml(meta_regression3_neg)[[1]]*100,1)`%.

```{r taxa-table-neg}

# getting marginal R2
r2_meta_regression3_neg <- r2_ml(meta_regression3_neg)

# getting estimates
# including back-transformation to ICC

mr3_neg <- mod_results(meta_regression3c_neg, mod="taxa")
mr3_mod_table_neg <- mr3_neg$mod_table

mr3_data_neg <- mr3_neg$data

# calculate k for each method separately

#df %>% group_by(taxa) %>% summarise(mean(k))

mr3_data_neg <- mr3_data_neg %>% mutate(k = case_when(moderator == "Landbird" ~ 2.68, 
                                              moderator == "Seabird" ~ 2.38,
                                              moderator == "Waterbird" ~ 3.08))

mr3_data_neg$yi_ICC <- Zr_to_ICC(mr3_data_neg$yi, mr3_data_neg$k)

mr3_mod_table_neg$k <- c(2.68, 2.38, 3.08)

for(i in names(mr3_mod_table_neg)[2:6]){
  
  mr3_mod_table_neg[i] <- Zr_to_ICC(mr3_mod_table_neg[i], mr3_mod_table_neg$k)
  
}

# creating a table
tibble(
  `Fixed effect` = c(rep(as.character(mr3_mod_table_neg$name),2), cont_gen(mr3_mod_table_neg$name)),
  `Unit` = c(rep(c("Zr (mu)", "ICC (mu)"),each = 3), rep("Zr (beta)", 3)),
  `Estimate` = c(meta_regression3c_neg$b, mr3_mod_table_neg$estimate, 
                 meta_regression3_neg$b[-1], meta_regression3b_neg$b[-(1:2)]),
  `Lower CI [0.025]` = c(meta_regression3c_neg$ci.lb, mr3_mod_table_neg$lowerCL,
                         meta_regression3_neg$ci.lb[-1], meta_regression3b_neg$ci.lb[-(1:2)]),
  `Upper CI  [0.975]` = c(meta_regression3c_neg$ci.ub, mr3_mod_table_neg$upperCL,
                         meta_regression3_neg$ci.ub[-1], meta_regression3b_neg$ci.ub[-(1:2)])) -> t_method_neg

t_method_neg %>% kable("html", digits = 3) %>%
  kable_styling("striped", position = "left") 

```

```{r meta-regression-k-neg}

meta_regression5_neg <- rma.mv(yi = Zr, V = VCV, 
                            mods = ~ k,
                            random = list(~1 | es_ID, 
                                          ~1 | paper_ID, 
                                          ~1 | cohort_ID, 
                                          ~1 | species_ID, 
                                          ~1 | phylogeny),
                            R = list(phylogeny = varcor),
                            data = df)
```

#### Table S17
Regression coefficients (Estimate) and 95% confidence intervals (CIs) in Zr from the meta-regression with `k` (number of observations per individual) when negative repeatability values are included. *R<sup>2</sup><sub>marginal</sub>* = `r round(orchaRd::r2_ml(meta_regression5_neg)[[1]]*100,1)`%.

```{r k-table-neg}
# getting marginal R2
r2_meta_regression5_neg <- r2_ml(meta_regression5_neg)

# creating a table
tibble(
  `Fixed effect` = c("Intercept", "k"),
  `Estimate` = c(meta_regression5_neg$b),
  `Lower CI [0.025]` = c(meta_regression5_neg$ci.lb),
  `Upper CI  [0.975]` = c(meta_regression5_neg$ci.ub)) -> t_k_neg

t_k_neg %>% kable("html", digits = 3) %>%
  kable_styling("striped", position = "left") 

```

```{r meta-regression-outlier-k-neg}
VCV2 <- impute_covariance_matrix(vi = df2$VZr, cluster = df2$cohort_ID, r = 0.5)

meta_regression6_neg <- rma.mv(yi = Zr, V = VCV2, 
                           mods = ~ k,
                           random = list(~1 | es_ID, 
                                         ~1 | paper_ID, 
                                         ~1 | cohort_ID, 
                                         ~1 | species_ID, 
                                         ~1 | phylogeny),
                           R = list(phylogeny = varcor), # added in phylogney
                           data = df2)
```

### Model selection with negative repeatability estimates included

Here we used the `MuMin` package to generate all possible moderator combinations (using all five variables: `annual_event`, `method`, `sex`, `taxa` & `k` (number of observations per individuals)), determine the importance of the moderators, and generate model-averaged estimates when the negative repeatability estimates are included.

```{r model-selection-neg}
eval(metafor:::.MuMIn)

full_model_MuMIn_neg <- rma.mv(yi = Zr, V = VCV, 
                          mods = ~ method + 
                            taxa + 
                            sex + 
                            annual_event +
                            k, 
                          random = list(~1 | es_ID, 
                                        ~1 | paper_ID, 
                                        ~1 | cohort_ID, 
                                        ~1 | species_ID, 
                                        ~1 | phylogeny),
                          R = list(phylogeny = varcor), 
                          method = "ML", # maximum likelihood for model selection
                          data = df)

# vif.rma(full_model_MuMIn)  # No major problems of collinearity (VIF <4)

candidate_models_neg <-dredge(full_model_MuMIn_neg) # Generate all possible combinations of moderators

candidates_aic2_neg <- subset(candidate_models_neg, delta<=2) # Display all models within 2 values of AICc

importance_neg <- sw(model.avg(candidate_models_neg, subset=delta<=2))# relative importance (sum of weights) of the moderators

mod.avg_neg <- summary(model.avg(candidate_models_neg, subset=delta<=2)) # Generate model-averaged estimates 

confidence_neg <- confint(mod.avg_neg, full=TRUE) # Generate confidence intervals for the estimates averaged using full-averages procedures

```

#### Table S18
The top six models (when negative repeatability values are included) within the $\Delta$AIC difference of 2, and which five variables: `annual_event`, `method`, `taxa`, `sex`, & `k` were included (indicated by $+$); model weights and the sum of weights for each of the variables are included. 

```{r model-selection-table-neg}
# creating a table
tibble(
  `Model (variable weight)` = c("Model1", "Model2", "Model3", "Model4", "Model5", "Model6", "(Sum of weights)"),
  annual_event = c(if_else(candidates_aic2_neg$annual_event == "+", "$+$", "NA"),round(importance_neg[1],3) ),
  sex = c(if_else(candidates_aic2_neg$sex== "+", "$+$", "NA"),round(importance_neg[2], 3)) ,
  taxa =  c(if_else(candidates_aic2_neg$taxa== "+", "$+$", "NA"),round(importance_neg[3], 3)),
  `method` = c(if_else(candidates_aic2_neg$method== "+", "$+$", "NA"),round(importance_neg[4], 3)),
  k = c(if_else(candidates_aic2_neg$k <= 0, "$+$", "NA"),round(importance_neg[5], 3)),
  delta_AICc = c(candidates_aic2_neg$delta, NA),
  Weight = c(candidates_aic2_neg$weight, NA)) %>% 
  kable("html", digits = 3) %>%
  kable_styling("striped", position = "left")
```

#### Model averaging

#### Table S19
The average estimates for regression coefficients (Estimate) and 95% confidence intervals (CIs) from the model averaging procedure when negative repeatability values are included using full-averages (assuming zero values for moderators when they do not occur).
```{r model-averaging-neg}
# creating a table
tibble(
  `Fixed effect` = c("Intercept",
                     "Depart_breed", "Nonbreed_arrival", "Nonbreed_depart", 
                     "Seabird", "Waterbird", "GLS", "Satellite",
                     "k","Female","Male"),
  Estimate = mod.avg_neg$coefmat.full[,1],
  `Lower CI [0.025]` = confidence_neg[,1],
  `Upper CI  [0.975]` =  confidence_neg[,2]) %>% 
  kable("html", digits = 3) %>%
  kable_styling("striped", position = "left")
```

## Publication Bias Analysis

We followed a recently proposed method by @nakagawa_methods_2021, which involved conducting 3 publication bias models: 1) Meta-regression with SE (uni-moderator), 2) meta-regression with year of publication (uni-moderator), and 3) all-in publication bias test (multi-moderator). We ran these publication bias models using the dataset that had negative repeatability values set to zero.

### Meta-regression with SE (uni-moderator)

To test for publication bias, we first fit a phylogenetic multilevel meta-regression to explore whether there is some evidence of small-study effects in our meta-analytic dataset. To do so, we fit a uni-moderator phylogenetic multilevel meta-regression including the effect sizes’ standard errors (sei) as the only moderator.

```{r small-study-model}
# creating a variable for the standard error of each effect size (i.e. the square root of the sampling variance)
df$sei <- sqrt(df$VZr)

# Application of Equation 21 from the main text in Nakagawa et al. 2021
publication.bias.model.r.se <- rma.mv(yi = Zr2, V = VCV,
                                      mod = ~1 + sei,
                                      random = list(~1 | es_ID, 
                                                    ~1 | paper_ID, 
                                                    ~1 | cohort_ID, 
                                                    ~1 | species_ID, 
                                                    ~1 | phylogeny),
                                      R = list(phylogeny = varcor), # added in phylogney
                                      data=df)

# print(publication.bias.model.r.se,digits=3)

```

#### Table S20
Regression coefficients (Estimate) and 95% confidence intervals (CIs) from the univariate meta-regression fitted with `sei`.

```{r small-study-table}

# creating a table
t_sei <- tibble(
  `Fixed effect` = c("Intercept", "sei"),
  `Estimate` = c(publication.bias.model.r.se$b),
  `Lower CI [0.025]` = c(publication.bias.model.r.se$ci.lb),
  `Upper CI  [0.975]` = c(publication.bias.model.r.se$ci.ub))
#  `R2` = c(orchaRd::r2_ml(publication.bias.model.r.se)[[1]], rep(NA,1)))

t_sei %>% kable("html", digits = 3) %>%
  kable_styling("striped", position = "left") 

```

According to this uni-moderator meta-regression, there is no evidence of small-study effects since the slope of the moderator ‘sei’ is not statistically significant (slope = `r round(publication.bias.model.r.se$b[2,1],3)`, 95% CI = [`r round(publication.bias.model.r.se$ci.lb[2],3)`, `r round(publication.bias.model.r.se$ci.ub[2],3)`]; *R<sup>2</sup><sub>marginal</sub>* = `r round(orchaRd::r2_ml(publication.bias.model.r.se)[[1]]*100,1)`%), showing that effect sizes with larger SE (more uncertain effect sizes) do not tend to be larger. But we will confirm this after accounting for some of the heterogeneity present in the data using the all-in publication bias test (multi-moderator meta-regression).

### Meta-regression with year of publication (uni-moderator)

To test for time-lag bias (also called decline effects) we can first fit a uni-moderator phylogenetic multilevel meta-regression including the year of publication (mean-centred) as the only moderator.

```{r publication-year-model}

df$pub_year.c <- as.vector(scale(df$pub_year, scale = F))

# Application of Equation 23 from the main text in Nakagawa et al. 2021
publication.bias.model.r.timelag <- rma.mv(yi = Zr2, V = VCV,
                                           mods= ~1 + pub_year.c,
                                           random = list(~1 | es_ID, 
                                                         ~1 | paper_ID, 
                                                         ~1 | cohort_ID, 
                                                         ~1 | species_ID, 
                                                         ~1 | phylogeny),
                                           R = list(phylogeny = varcor), # added in phylogney
                                           data=df)

```

#### Table S21
Regression coefficients (Estimate) and 95% confidence intervals (CIs) from the univariate meta-regression fitted with publication year.

```{r publication-year-table}

# creating a table
t_pub_year <- tibble(
  `Fixed effect` = c("Intercept", "pub_year.c"),
  `Estimate` = c(publication.bias.model.r.timelag$b),
  `Lower CI [0.025]` = c(publication.bias.model.r.timelag$ci.lb),
  `Upper CI  [0.975]` = c(publication.bias.model.r.timelag$ci.ub))
#  `R2` = c(orchaRd::r2_ml(publication.bias.model.r.timelag)[[1]], rep(NA,1)))

t_pub_year %>% kable("html", digits = 3) %>%
  kable_styling("striped", position = "left") 

```

According to this uni-moderator meta-regression, there is no decline effects since the slope of the moderator ‘year of publication’ is essentially zero (slope = `r round(publication.bias.model.r.timelag$b[2,1],3)`, 95% CI = [`r round(publication.bias.model.r.timelag$ci.lb[2],3)`, `r round(publication.bias.model.r.timelag$ci.ub[2],3)`]; *R<sup>2</sup><sub>marginal</sub>* = `r round(orchaRd::r2_ml(publication.bias.model.r.timelag)[[1]]*100,1)`%), showing that effect sizes have not changed linearly over time since the first effect size was published. But again, we need to confirm this pattern after accounting for some of the heterogeneity present in the data using the all-in publication bias test.


### All-in publication bias test (multi-moderator)

When heterogeneity exists (which is normally the case in ecology and evolution; Senior et al. 2016), it is best to combine the above two models with other moderators since those additional moderators will generally be expected to explain some of the heterogeneity. That is, this all-in publication bias test (multi-moderator meta-regression) would be the best test of small-study (publication bias) and decline effects (time-lag bias) in most meta-analytic datasets [see @nakagawa_methods_2021]. For our data, we will run a multi-moderator phylogenetic multilevel meta-regression including the effect sizes’ standard errors, the year of publication (mean-centred) and the 5 moderators included in previous models.

```{r all-in-publication-model}

publication.bias.model.r.all.se <- rma.mv(yi = Zr2, V = VCV,
                                         mods= ~1 + # -1 removes the intercept
                                           sei +
                                           pub_year.c +
                                         annual_event + method + taxa + sex + k, 
                                         random = list(~1 | es_ID, 
                                                       ~1 | paper_ID, 
                                                       ~1 | cohort_ID, 
                                                       ~1 | species_ID, 
                                                       ~1 | phylogeny),
                                         R = list(phylogeny = varcor), # added in phylogney
                                         data=df)

```

#### Table S22
Regression coefficients (Estimate) and 95% confidence intervals (CIs) from the multivariate meta-regression with sei, publication year, and the five moderator variables. *R<sup>2</sup><sub>marginal</sub>* = `r round(orchaRd::r2_ml(publication.bias.model.r.all.se)[[1]]*100,1)`%.

```{r all-in-publication-table}

# creating a table
t_all_in <- tibble(
  `Fixed effect` = c("Intercept", "sei", "pub_year.c", "Depart_breed", "Nonbreed_arrival", "Nonbreed_depart", "GLS", "Satellite", "Seabird", "Waterbird", "Female", "Male", "k"),
  `Estimate` = c(publication.bias.model.r.all.se$b),
  `Lower CI [0.025]` = c(publication.bias.model.r.all.se$ci.lb),
  `Upper CI  [0.975]` = c(publication.bias.model.r.all.se$ci.ub))
 # `R2` = c(r2_publication.bias.model.r.all.se[1], rep(NA,12)))

t_all_in %>% kable("html", digits = 3) %>%
  kable_styling("striped", position = "left") 
```

The all-in publication bias test agrees with what we observed in the uni-moderator meta-regressions above. First, the multi-moderator meta-regression shows no significant slope for the moderator ‘sei’ (slope = `r round(publication.bias.model.r.all.se$b[2,1],3)`, 95% CI = [`r round(publication.bias.model.r.all.se$ci.lb[2],3)`,`r round(publication.bias.model.r.all.se$ci.ub[2],3)`]; Fig. S5), showing no evidence of small-study effects. In other words, the largest effect sizes in the dataset do not tend to be those with the lowest precision (i.e. larger uncertainty). Second, the all-in publication bias test also confirms that there is no evidence of decline effects in the data since the slope of the moderator ‘year of publication’ was again indistinguishable from zero (slope = `r round(publication.bias.model.r.all.se$b[3,1],3)`, 95% CI = [`r round(publication.bias.model.r.all.se$ci.lb[3],3)`,`r round(publication.bias.model.r.all.se$ci.ub[3],3)`]; Fig. S6).

#### Figure S5

```{r figureS5}

predict.publication.bias.model.r.all.se.plot.1 <- predict(publication.bias.model.r.all.se,
                                                          newmods=cbind(seq(min(df$sei),
                                                                            max(df$sei),
                                                                            0.005),
                                                                        c(0), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0))

newdat <- data.frame(sei=seq(min(df$sei),
                             max(df$sei),
                             0.005),
                     fit=predict.publication.bias.model.r.all.se.plot.1$pred,
                     upper=predict.publication.bias.model.r.all.se.plot.1$ci.ub,
                     lower=predict.publication.bias.model.r.all.se.plot.1$ci.lb,
                     stringsAsFactors=FALSE)

ggplot(data = df, aes(x = sei, y = Zr2)) +
  geom_point(shape = 21, fill = "grey85", colour="grey60", size=3, alpha=0.5) +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5) +
  geom_line(data=newdat, aes(x=sei, y=fit), size = 1.5, colour="darkorchid4") +
  geom_ribbon(data=newdat, aes(ymin = lower, ymax = upper, y=0), alpha = .3, fill="darkorchid4") +
  labs(x="Standard error (sei)", y="Effect size (Zr)") +
  scale_y_continuous(limits=c(-1, 2.5), breaks = seq(-1, 2.2, by = 1)) +
  theme_bw() +
  theme(text = element_text(size = 9, colour = "black", hjust = 0.5),
        panel.grid = element_blank())

```
  
  **Figure S5.** A bubble plot showing that effect sizes with larger standard errors do not tend to be larger, providing no evidence of small-study effects in the meta-analytic dataset. The solid line represents the model estimate and the shading shows its 95% confidence intervals.

#### Figure S6

```{r figureS6}

predict.publication.bias.model.r.all.se.plot.2 <- predict(publication.bias.model.r.all.se,
                                                          newmods=cbind(mean(df$sei),
                                                                        seq(min(df$pub_year.c),
                                                                            max(df$pub_year.c),
                                                                            0.25),0, 0,0,0,0,0,0,0,0,0))


newdat2 <- data.frame(pub_year.c=seq(min(df$pub_year.c),
                              max(df$pub_year.c),
                              0.25),
                     fit=predict.publication.bias.model.r.all.se.plot.2$pred,
                     upper=predict.publication.bias.model.r.all.se.plot.2$ci.ub,
                     lower=predict.publication.bias.model.r.all.se.plot.2$ci.lb,
                     stringsAsFactors=FALSE)

ggplot(data = df, aes(x = pub_year.c, y = Zr2)) +
  geom_point(aes(size=(1/sqrt(VZr))), shape = 21, fill = "grey85", colour="grey60", alpha=0.5) +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5) +
  geom_line(data=newdat2, aes(x=pub_year.c, y=fit), size = 1.5, colour="darkorchid4") +
  geom_ribbon(data=newdat2, aes(ymin = lower, ymax = upper, y=0), alpha = .3, fill="darkorchid4") +
  labs(x="Year of publication", y="Effect size (Zr)", size = "Precison (1/SE)") +
  scale_y_continuous(limits=c(-1, 2.5), breaks = seq(-1, 2.2, by = 1)) +
  scale_x_continuous(breaks = c(-24.0862069, -14.0862069, -4.0862069, 5.9137931), label = c(1990,2000, 2010, 2020)) +
  theme_bw() +
  theme(text = element_text(size = 9, colour = "black", hjust = 0.5),
        panel.grid = element_blank(),
        legend.text=element_text(size=8),
          legend.position=c(0,0), 
          legend.justification = c(0,0),
          legend.background = element_blank(), 
          legend.direction="horizontal",
          legend.title = element_text(size=8))

```
  
  **Figure S6.** A bubble plot showing that the overall effect size has not changed over time, where the solid line represents the model estimate and the shading shows its 95% confidence intervals, with individual data points scaled by precision (1/SE).

## Acknowledgements

Many coding materials have been borrowed from these papers [@hayward_broadscale_2021; @pottier_sexual_2021].

## R Session Information

```{r echo=FALSE}
# pander for making it look nicer
sessionInfo() %>% pander()
```


## References